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  1. paper-releases/pbisc-diffusion/updates/20261002T124820Z-crosscohort-liu-default/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/ho_ji2025_chili/K1000/lora16/train.log +83 -0
  2. paper-releases/pbisc-diffusion/updates/20261002T124820Z-crosscohort-liu-default/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/ho_ye2026_sepsis/K1000/lora16/train.log +83 -0
  3. paper-releases/pbisc-diffusion/updates/20261002T124820Z-crosscohort-liu-default/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/AGGREGATE.md +49 -0
  4. paper-releases/pbisc-diffusion/updates/20261002T124820Z-crosscohort-liu-default/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/aggregate.csv +21 -0
paper-releases/pbisc-diffusion/updates/20261002T124820Z-crosscohort-liu-default/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/ho_ji2025_chili/K1000/lora16/train.log ADDED
@@ -0,0 +1,83 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2026-10-02T20:52:51 split=ho_ji2025_chili K=1000 mode=lora trainable=1,296,224 balance=cohort immicro: train 95 rows / 347,061 cells, inner 12; ye2026_sepsis: train 73 rows / 529,816 cells, inner 9; imperial_eskd: train 146 rows / 462,172 cells, inner 11; giroux2022: train 24 rows / 254,250 cells, inner 0
2
+ 2026-10-02T20:52:53 step 0 inner-val loss 0.11338 (pretrained) {'immicro': 0.10636159498244524, 'ye2026_sepsis': 0.12489082664251328, 'imperial_eskd': 0.10889218679883263}
3
+ 2026-10-02T20:53:04 step 250 train 0.10532 inner-val 0.11269 best 0.11269@250 (10s) immicro=0.1057 ye2026_sepsis=0.1242 imperial_eskd=0.1081
4
+ 2026-10-02T20:53:14 step 500 train 0.10238 inner-val 0.11101 best 0.11101@500 (20s) immicro=0.1043 ye2026_sepsis=0.1226 imperial_eskd=0.1062
5
+ 2026-10-02T20:53:24 step 750 train 0.10229 inner-val 0.10911 best 0.10911@750 (31s) immicro=0.1029 ye2026_sepsis=0.1204 imperial_eskd=0.1041
6
+ 2026-10-02T20:53:34 step 1000 train 0.10194 inner-val 0.10750 best 0.10750@1000 (41s) immicro=0.1018 ye2026_sepsis=0.1183 imperial_eskd=0.1024
7
+ 2026-10-02T20:53:45 step 1250 train 0.10189 inner-val 0.10637 best 0.10637@1250 (51s) immicro=0.1010 ye2026_sepsis=0.1168 imperial_eskd=0.1013
8
+ 2026-10-02T20:53:55 step 1500 train 0.10142 inner-val 0.10566 best 0.10566@1500 (62s) immicro=0.1004 ye2026_sepsis=0.1158 imperial_eskd=0.1007
9
+ 2026-10-02T20:54:05 step 1750 train 0.10157 inner-val 0.10520 best 0.10520@1750 (72s) immicro=0.1000 ye2026_sepsis=0.1153 imperial_eskd=0.1003
10
+ 2026-10-02T20:54:15 step 2000 train 0.10134 inner-val 0.10489 best 0.10489@2000 (82s) immicro=0.0997 ye2026_sepsis=0.1149 imperial_eskd=0.1001
11
+ 2026-10-02T20:54:25 step 2250 train 0.10135 inner-val 0.10467 best 0.10467@2250 (92s) immicro=0.0995 ye2026_sepsis=0.1147 imperial_eskd=0.0999
12
+ 2026-10-02T20:54:36 step 2500 train 0.10145 inner-val 0.10452 best 0.10452@2500 (102s) immicro=0.0993 ye2026_sepsis=0.1145 imperial_eskd=0.0998
13
+ 2026-10-02T20:54:46 step 2750 train 0.10133 inner-val 0.10441 best 0.10441@2750 (113s) immicro=0.0992 ye2026_sepsis=0.1144 imperial_eskd=0.0997
14
+ 2026-10-02T20:54:56 step 3000 train 0.10156 inner-val 0.10433 best 0.10433@3000 (123s) immicro=0.0991 ye2026_sepsis=0.1143 imperial_eskd=0.0996
15
+ 2026-10-02T20:55:06 step 3250 train 0.10144 inner-val 0.10427 best 0.10427@3250 (133s) immicro=0.0990 ye2026_sepsis=0.1142 imperial_eskd=0.0996
16
+ 2026-10-02T20:55:17 step 3500 train 0.10139 inner-val 0.10422 best 0.10422@3500 (143s) immicro=0.0990 ye2026_sepsis=0.1141 imperial_eskd=0.0996
17
+ 2026-10-02T20:55:27 step 3750 train 0.10125 inner-val 0.10419 best 0.10419@3750 (153s) immicro=0.0989 ye2026_sepsis=0.1141 imperial_eskd=0.0995
18
+ 2026-10-02T20:55:37 step 4000 train 0.10126 inner-val 0.10416 best 0.10416@4000 (163s) immicro=0.0989 ye2026_sepsis=0.1141 imperial_eskd=0.0995
19
+ 2026-10-02T20:55:47 step 4250 train 0.10161 inner-val 0.10413 best 0.10413@4250 (174s) immicro=0.0989 ye2026_sepsis=0.1140 imperial_eskd=0.0995
20
+ 2026-10-02T20:55:57 step 4500 train 0.10122 inner-val 0.10411 best 0.10411@4500 (184s) immicro=0.0989 ye2026_sepsis=0.1140 imperial_eskd=0.0995
21
+ 2026-10-02T20:56:07 step 4750 train 0.10145 inner-val 0.10409 best 0.10409@4750 (194s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0995
22
+ 2026-10-02T20:56:17 step 5000 train 0.10148 inner-val 0.10408 best 0.10408@5000 (204s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0995
23
+ 2026-10-02T20:56:28 step 5250 train 0.10119 inner-val 0.10406 best 0.10406@5250 (214s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994
24
+ 2026-10-02T20:56:38 step 5500 train 0.10120 inner-val 0.10405 best 0.10405@5500 (224s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994
25
+ 2026-10-02T20:56:48 step 5750 train 0.10096 inner-val 0.10404 best 0.10404@5750 (234s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994
26
+ 2026-10-02T20:56:58 step 6000 train 0.10108 inner-val 0.10403 best 0.10403@6000 (245s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994
27
+ 2026-10-02T20:57:08 step 6250 train 0.10137 inner-val 0.10401 best 0.10401@6250 (255s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994
28
+ 2026-10-02T20:57:19 step 6500 train 0.10143 inner-val 0.10401 best 0.10401@6500 (265s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994
29
+ 2026-10-02T20:57:29 step 6750 train 0.10110 inner-val 0.10400 best 0.10400@6750 (275s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0994
30
+ 2026-10-02T20:57:39 step 7000 train 0.10131 inner-val 0.10399 best 0.10399@7000 (286s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0994
31
+ 2026-10-02T20:57:49 step 7250 train 0.10146 inner-val 0.10398 best 0.10398@7250 (296s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0994
32
+ 2026-10-02T20:57:59 step 7500 train 0.10111 inner-val 0.10397 best 0.10397@7500 (306s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
33
+ 2026-10-02T20:58:10 step 7750 train 0.10104 inner-val 0.10397 best 0.10397@7750 (316s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
34
+ 2026-10-02T20:58:20 step 8000 train 0.10100 inner-val 0.10396 best 0.10396@8000 (327s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
35
+ 2026-10-02T20:58:30 step 8250 train 0.10145 inner-val 0.10396 best 0.10396@8250 (337s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
36
+ 2026-10-02T20:58:40 step 8500 train 0.10094 inner-val 0.10395 best 0.10395@8500 (347s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
37
+ 2026-10-02T20:58:50 step 8750 train 0.10105 inner-val 0.10395 best 0.10395@8750 (357s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
38
+ 2026-10-02T20:59:01 step 9000 train 0.10124 inner-val 0.10395 best 0.10395@9000 (367s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
39
+ 2026-10-02T20:59:11 step 9250 train 0.10114 inner-val 0.10395 best 0.10395@9000 (378s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
40
+ 2026-10-02T20:59:21 step 9500 train 0.10105 inner-val 0.10394 best 0.10394@9500 (388s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
41
+ 2026-10-02T20:59:33 step 9750 train 0.10105 inner-val 0.10394 best 0.10394@9750 (400s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
42
+ 2026-10-02T20:59:45 step 10000 train 0.10099 inner-val 0.10393 best 0.10393@10000 (412s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
43
+ 2026-10-02T20:59:57 step 10250 train 0.10103 inner-val 0.10393 best 0.10393@10250 (423s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993
44
+ 2026-10-02T21:00:08 step 10500 train 0.10104 inner-val 0.10392 best 0.10392@10500 (435s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0992
45
+ 2026-10-02T21:00:20 step 10750 train 0.10112 inner-val 0.10392 best 0.10392@10750 (447s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0992
46
+ 2026-10-02T21:00:31 step 11000 train 0.10134 inner-val 0.10392 best 0.10392@11000 (457s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0992
47
+ 2026-10-02T21:00:42 step 11250 train 0.10091 inner-val 0.10392 best 0.10392@11250 (469s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0992
48
+ 2026-10-02T21:00:54 step 11500 train 0.10114 inner-val 0.10392 best 0.10392@11250 (481s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
49
+ 2026-10-02T21:01:06 step 11750 train 0.10099 inner-val 0.10392 best 0.10392@11250 (493s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
50
+ 2026-10-02T21:01:18 step 12000 train 0.10080 inner-val 0.10392 best 0.10392@11250 (505s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
51
+ 2026-10-02T21:01:30 step 12250 train 0.10083 inner-val 0.10391 best 0.10391@12250 (517s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
52
+ 2026-10-02T21:01:41 step 12500 train 0.10120 inner-val 0.10391 best 0.10391@12500 (528s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
53
+ 2026-10-02T21:01:51 step 12750 train 0.10090 inner-val 0.10391 best 0.10391@12750 (538s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
54
+ 2026-10-02T21:02:01 step 13000 train 0.10116 inner-val 0.10391 best 0.10391@13000 (548s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
55
+ 2026-10-02T21:02:12 step 13250 train 0.10116 inner-val 0.10391 best 0.10391@13250 (558s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
56
+ 2026-10-02T21:02:22 step 13500 train 0.10103 inner-val 0.10390 best 0.10390@13500 (569s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
57
+ 2026-10-02T21:02:33 step 13750 train 0.10120 inner-val 0.10390 best 0.10390@13750 (579s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
58
+ 2026-10-02T21:02:44 step 14000 train 0.10120 inner-val 0.10390 best 0.10390@13750 (590s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
59
+ 2026-10-02T21:02:55 step 14250 train 0.10091 inner-val 0.10390 best 0.10390@13750 (602s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
60
+ 2026-10-02T21:03:07 step 14500 train 0.10123 inner-val 0.10390 best 0.10390@14500 (613s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
61
+ 2026-10-02T21:03:17 step 14750 train 0.10106 inner-val 0.10390 best 0.10390@14500 (624s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
62
+ 2026-10-02T21:03:27 step 15000 train 0.10110 inner-val 0.10389 best 0.10389@15000 (634s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
63
+ 2026-10-02T21:03:38 step 15250 train 0.10101 inner-val 0.10389 best 0.10389@15250 (644s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
64
+ 2026-10-02T21:03:49 step 15500 train 0.10118 inner-val 0.10389 best 0.10389@15500 (655s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
65
+ 2026-10-02T21:04:00 step 15750 train 0.10092 inner-val 0.10388 best 0.10388@15750 (667s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
66
+ 2026-10-02T21:04:12 step 16000 train 0.10091 inner-val 0.10388 best 0.10388@16000 (678s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
67
+ 2026-10-02T21:04:23 step 16250 train 0.10122 inner-val 0.10388 best 0.10388@16000 (690s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
68
+ 2026-10-02T21:04:34 step 16500 train 0.10111 inner-val 0.10388 best 0.10388@16500 (701s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
69
+ 2026-10-02T21:04:45 step 16750 train 0.10083 inner-val 0.10387 best 0.10387@16750 (712s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
70
+ 2026-10-02T21:04:57 step 17000 train 0.10102 inner-val 0.10387 best 0.10387@17000 (724s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
71
+ 2026-10-02T21:05:09 step 17250 train 0.10107 inner-val 0.10388 best 0.10387@17000 (736s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
72
+ 2026-10-02T21:05:21 step 17500 train 0.10070 inner-val 0.10387 best 0.10387@17000 (748s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
73
+ 2026-10-02T21:05:32 step 17750 train 0.10055 inner-val 0.10388 best 0.10387@17000 (759s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
74
+ 2026-10-02T21:05:42 step 18000 train 0.10137 inner-val 0.10388 best 0.10387@17000 (769s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
75
+ 2026-10-02T21:05:53 step 18250 train 0.10061 inner-val 0.10387 best 0.10387@17000 (779s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
76
+ 2026-10-02T21:06:03 step 18500 train 0.10098 inner-val 0.10387 best 0.10387@17000 (790s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
77
+ 2026-10-02T21:06:13 step 18750 train 0.10062 inner-val 0.10387 best 0.10387@18750 (800s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
78
+ 2026-10-02T21:06:23 step 19000 train 0.10091 inner-val 0.10387 best 0.10387@19000 (810s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992
79
+ 2026-10-02T21:06:33 step 19250 train 0.10113 inner-val 0.10386 best 0.10386@19250 (820s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991
80
+ 2026-10-02T21:06:43 step 19500 train 0.10083 inner-val 0.10386 best 0.10386@19500 (830s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991
81
+ 2026-10-02T21:06:53 step 19750 train 0.10088 inner-val 0.10386 best 0.10386@19500 (840s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991
82
+ 2026-10-02T21:07:04 step 20000 train 0.10092 inner-val 0.10386 best 0.10386@19500 (850s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992
83
+ 2026-10-02T21:07:07 saved /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/ho_ji2025_chili/K1000/lora16/checkpoint_ft.pt (selected step 19500, inner-val 0.10386 vs pretrained 0.11338); sha e00f716f4a61
paper-releases/pbisc-diffusion/updates/20261002T124820Z-crosscohort-liu-default/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/ho_ye2026_sepsis/K1000/lora16/train.log ADDED
@@ -0,0 +1,83 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2026-10-02T21:09:26 split=ho_ye2026_sepsis K=1000 mode=lora trainable=1,296,224 balance=cohort immicro: train 95 rows / 347,061 cells, inner 12; imperial_eskd: train 146 rows / 462,172 cells, inner 11; giroux2022: train 24 rows / 254,250 cells, inner 0
2
+ 2026-10-02T21:09:27 step 0 inner-val loss 0.10749 (pretrained) {'immicro': 0.10636159498244524, 'imperial_eskd': 0.1086193590678952}
3
+ 2026-10-02T21:09:37 step 250 train 0.10194 inner-val 0.10678 best 0.10678@250 (10s) immicro=0.1057 imperial_eskd=0.1078
4
+ 2026-10-02T21:09:47 step 500 train 0.09891 inner-val 0.10508 best 0.10508@500 (20s) immicro=0.1042 imperial_eskd=0.1060
5
+ 2026-10-02T21:09:56 step 750 train 0.09920 inner-val 0.10333 best 0.10333@750 (29s) immicro=0.1027 imperial_eskd=0.1039
6
+ 2026-10-02T21:10:06 step 1000 train 0.09845 inner-val 0.10191 best 0.10191@1000 (39s) immicro=0.1016 imperial_eskd=0.1022
7
+ 2026-10-02T21:10:15 step 1250 train 0.09866 inner-val 0.10093 best 0.10093@1250 (48s) immicro=0.1008 imperial_eskd=0.1011
8
+ 2026-10-02T21:10:25 step 1500 train 0.09840 inner-val 0.10027 best 0.10027@1500 (58s) immicro=0.1002 imperial_eskd=0.1003
9
+ 2026-10-02T21:10:34 step 1750 train 0.09859 inner-val 0.09984 best 0.09984@1750 (67s) immicro=0.0998 imperial_eskd=0.0999
10
+ 2026-10-02T21:10:44 step 2000 train 0.09830 inner-val 0.09955 best 0.09955@2000 (77s) immicro=0.0995 imperial_eskd=0.0996
11
+ 2026-10-02T21:10:54 step 2250 train 0.09819 inner-val 0.09936 best 0.09936@2250 (87s) immicro=0.0993 imperial_eskd=0.0994
12
+ 2026-10-02T21:11:03 step 2500 train 0.09835 inner-val 0.09923 best 0.09923@2500 (96s) immicro=0.0991 imperial_eskd=0.0993
13
+ 2026-10-02T21:11:13 step 2750 train 0.09832 inner-val 0.09914 best 0.09914@2750 (106s) immicro=0.0990 imperial_eskd=0.0993
14
+ 2026-10-02T21:11:23 step 3000 train 0.09831 inner-val 0.09907 best 0.09907@3000 (116s) immicro=0.0989 imperial_eskd=0.0992
15
+ 2026-10-02T21:11:32 step 3250 train 0.09832 inner-val 0.09902 best 0.09902@3250 (125s) immicro=0.0989 imperial_eskd=0.0992
16
+ 2026-10-02T21:11:42 step 3500 train 0.09861 inner-val 0.09899 best 0.09899@3500 (135s) immicro=0.0988 imperial_eskd=0.0991
17
+ 2026-10-02T21:11:52 step 3750 train 0.09823 inner-val 0.09896 best 0.09896@3750 (145s) immicro=0.0988 imperial_eskd=0.0991
18
+ 2026-10-02T21:12:02 step 4000 train 0.09818 inner-val 0.09893 best 0.09893@4000 (155s) immicro=0.0988 imperial_eskd=0.0991
19
+ 2026-10-02T21:12:13 step 4250 train 0.09833 inner-val 0.09891 best 0.09891@4250 (166s) immicro=0.0988 imperial_eskd=0.0991
20
+ 2026-10-02T21:12:24 step 4500 train 0.09787 inner-val 0.09889 best 0.09889@4500 (177s) immicro=0.0987 imperial_eskd=0.0990
21
+ 2026-10-02T21:12:36 step 4750 train 0.09826 inner-val 0.09887 best 0.09887@4750 (189s) immicro=0.0987 imperial_eskd=0.0990
22
+ 2026-10-02T21:12:47 step 5000 train 0.09814 inner-val 0.09885 best 0.09885@5000 (200s) immicro=0.0987 imperial_eskd=0.0990
23
+ 2026-10-02T21:12:58 step 5250 train 0.09811 inner-val 0.09884 best 0.09884@5250 (211s) immicro=0.0987 imperial_eskd=0.0990
24
+ 2026-10-02T21:13:10 step 5500 train 0.09810 inner-val 0.09882 best 0.09882@5500 (222s) immicro=0.0987 imperial_eskd=0.0990
25
+ 2026-10-02T21:13:21 step 5750 train 0.09800 inner-val 0.09880 best 0.09880@5750 (234s) immicro=0.0987 imperial_eskd=0.0989
26
+ 2026-10-02T21:13:32 step 6000 train 0.09791 inner-val 0.09879 best 0.09879@6000 (245s) immicro=0.0987 imperial_eskd=0.0989
27
+ 2026-10-02T21:13:43 step 6250 train 0.09816 inner-val 0.09878 best 0.09878@6250 (256s) immicro=0.0986 imperial_eskd=0.0989
28
+ 2026-10-02T21:13:55 step 6500 train 0.09810 inner-val 0.09876 best 0.09876@6500 (268s) immicro=0.0986 imperial_eskd=0.0989
29
+ 2026-10-02T21:14:06 step 6750 train 0.09801 inner-val 0.09875 best 0.09875@6750 (279s) immicro=0.0986 imperial_eskd=0.0989
30
+ 2026-10-02T21:14:17 step 7000 train 0.09797 inner-val 0.09874 best 0.09874@7000 (290s) immicro=0.0986 imperial_eskd=0.0989
31
+ 2026-10-02T21:14:28 step 7250 train 0.09799 inner-val 0.09874 best 0.09874@7250 (301s) immicro=0.0986 imperial_eskd=0.0989
32
+ 2026-10-02T21:14:39 step 7500 train 0.09799 inner-val 0.09873 best 0.09873@7500 (312s) immicro=0.0986 imperial_eskd=0.0989
33
+ 2026-10-02T21:14:51 step 7750 train 0.09806 inner-val 0.09872 best 0.09872@7750 (324s) immicro=0.0986 imperial_eskd=0.0988
34
+ 2026-10-02T21:15:02 step 8000 train 0.09771 inner-val 0.09871 best 0.09871@8000 (335s) immicro=0.0986 imperial_eskd=0.0988
35
+ 2026-10-02T21:15:14 step 8250 train 0.09799 inner-val 0.09871 best 0.09871@8250 (346s) immicro=0.0986 imperial_eskd=0.0988
36
+ 2026-10-02T21:15:25 step 8500 train 0.09753 inner-val 0.09870 best 0.09870@8500 (358s) immicro=0.0986 imperial_eskd=0.0988
37
+ 2026-10-02T21:15:36 step 8750 train 0.09780 inner-val 0.09870 best 0.09870@8750 (369s) immicro=0.0986 imperial_eskd=0.0988
38
+ 2026-10-02T21:15:47 step 9000 train 0.09818 inner-val 0.09870 best 0.09870@9000 (380s) immicro=0.0986 imperial_eskd=0.0988
39
+ 2026-10-02T21:15:59 step 9250 train 0.09797 inner-val 0.09869 best 0.09869@9250 (392s) immicro=0.0986 imperial_eskd=0.0988
40
+ 2026-10-02T21:16:10 step 9500 train 0.09798 inner-val 0.09868 best 0.09868@9500 (403s) immicro=0.0985 imperial_eskd=0.0988
41
+ 2026-10-02T21:16:21 step 9750 train 0.09764 inner-val 0.09868 best 0.09868@9750 (414s) immicro=0.0985 imperial_eskd=0.0988
42
+ 2026-10-02T21:16:33 step 10000 train 0.09783 inner-val 0.09867 best 0.09867@10000 (426s) immicro=0.0985 imperial_eskd=0.0988
43
+ 2026-10-02T21:16:44 step 10250 train 0.09794 inner-val 0.09867 best 0.09867@10250 (437s) immicro=0.0985 imperial_eskd=0.0988
44
+ 2026-10-02T21:16:55 step 10500 train 0.09795 inner-val 0.09866 best 0.09866@10500 (448s) immicro=0.0985 imperial_eskd=0.0988
45
+ 2026-10-02T21:17:07 step 10750 train 0.09773 inner-val 0.09866 best 0.09866@10750 (460s) immicro=0.0985 imperial_eskd=0.0988
46
+ 2026-10-02T21:17:18 step 11000 train 0.09808 inner-val 0.09865 best 0.09865@11000 (471s) immicro=0.0985 imperial_eskd=0.0988
47
+ 2026-10-02T21:17:30 step 11250 train 0.09782 inner-val 0.09865 best 0.09865@11250 (483s) immicro=0.0985 imperial_eskd=0.0988
48
+ 2026-10-02T21:17:41 step 11500 train 0.09803 inner-val 0.09865 best 0.09865@11500 (494s) immicro=0.0985 imperial_eskd=0.0988
49
+ 2026-10-02T21:17:52 step 11750 train 0.09767 inner-val 0.09864 best 0.09864@11750 (505s) immicro=0.0985 imperial_eskd=0.0988
50
+ 2026-10-02T21:18:03 step 12000 train 0.09751 inner-val 0.09864 best 0.09864@12000 (516s) immicro=0.0985 imperial_eskd=0.0988
51
+ 2026-10-02T21:18:15 step 12250 train 0.09776 inner-val 0.09864 best 0.09864@12250 (528s) immicro=0.0985 imperial_eskd=0.0988
52
+ 2026-10-02T21:18:26 step 12500 train 0.09813 inner-val 0.09863 best 0.09863@12500 (539s) immicro=0.0985 imperial_eskd=0.0988
53
+ 2026-10-02T21:18:37 step 12750 train 0.09785 inner-val 0.09863 best 0.09863@12750 (550s) immicro=0.0985 imperial_eskd=0.0988
54
+ 2026-10-02T21:18:48 step 13000 train 0.09799 inner-val 0.09863 best 0.09863@12750 (561s) immicro=0.0985 imperial_eskd=0.0988
55
+ 2026-10-02T21:19:00 step 13250 train 0.09793 inner-val 0.09862 best 0.09862@13250 (573s) immicro=0.0985 imperial_eskd=0.0988
56
+ 2026-10-02T21:19:11 step 13500 train 0.09785 inner-val 0.09862 best 0.09862@13500 (584s) immicro=0.0985 imperial_eskd=0.0988
57
+ 2026-10-02T21:19:22 step 13750 train 0.09798 inner-val 0.09862 best 0.09862@13750 (595s) immicro=0.0985 imperial_eskd=0.0988
58
+ 2026-10-02T21:19:33 step 14000 train 0.09799 inner-val 0.09862 best 0.09862@14000 (606s) immicro=0.0985 imperial_eskd=0.0988
59
+ 2026-10-02T21:19:45 step 14250 train 0.09752 inner-val 0.09862 best 0.09862@14250 (618s) immicro=0.0985 imperial_eskd=0.0988
60
+ 2026-10-02T21:19:56 step 14500 train 0.09796 inner-val 0.09862 best 0.09862@14250 (629s) immicro=0.0985 imperial_eskd=0.0988
61
+ 2026-10-02T21:20:07 step 14750 train 0.09786 inner-val 0.09861 best 0.09861@14750 (640s) immicro=0.0985 imperial_eskd=0.0988
62
+ 2026-10-02T21:20:19 step 15000 train 0.09792 inner-val 0.09861 best 0.09861@14750 (652s) immicro=0.0985 imperial_eskd=0.0988
63
+ 2026-10-02T21:20:30 step 15250 train 0.09787 inner-val 0.09861 best 0.09861@15250 (663s) immicro=0.0985 imperial_eskd=0.0988
64
+ 2026-10-02T21:20:41 step 15500 train 0.09809 inner-val 0.09861 best 0.09861@15500 (674s) immicro=0.0985 imperial_eskd=0.0988
65
+ 2026-10-02T21:20:53 step 15750 train 0.09769 inner-val 0.09861 best 0.09861@15750 (686s) immicro=0.0984 imperial_eskd=0.0988
66
+ 2026-10-02T21:21:03 step 16000 train 0.09757 inner-val 0.09860 best 0.09860@16000 (696s) immicro=0.0984 imperial_eskd=0.0988
67
+ 2026-10-02T21:21:13 step 16250 train 0.09793 inner-val 0.09860 best 0.09860@16000 (706s) immicro=0.0984 imperial_eskd=0.0988
68
+ 2026-10-02T21:21:23 step 16500 train 0.09806 inner-val 0.09860 best 0.09860@16500 (716s) immicro=0.0984 imperial_eskd=0.0988
69
+ 2026-10-02T21:21:34 step 16750 train 0.09783 inner-val 0.09860 best 0.09860@16750 (727s) immicro=0.0984 imperial_eskd=0.0988
70
+ 2026-10-02T21:21:45 step 17000 train 0.09801 inner-val 0.09860 best 0.09860@16750 (738s) immicro=0.0984 imperial_eskd=0.0988
71
+ 2026-10-02T21:21:56 step 17250 train 0.09787 inner-val 0.09859 best 0.09859@17250 (749s) immicro=0.0984 imperial_eskd=0.0988
72
+ 2026-10-02T21:22:08 step 17500 train 0.09766 inner-val 0.09859 best 0.09859@17500 (761s) immicro=0.0984 imperial_eskd=0.0988
73
+ 2026-10-02T21:22:18 step 17750 train 0.09761 inner-val 0.09858 best 0.09858@17750 (771s) immicro=0.0984 imperial_eskd=0.0987
74
+ 2026-10-02T21:22:28 step 18000 train 0.09809 inner-val 0.09858 best 0.09858@17750 (781s) immicro=0.0984 imperial_eskd=0.0987
75
+ 2026-10-02T21:22:39 step 18250 train 0.09764 inner-val 0.09858 best 0.09858@18250 (792s) immicro=0.0984 imperial_eskd=0.0987
76
+ 2026-10-02T21:22:51 step 18500 train 0.09751 inner-val 0.09858 best 0.09858@18500 (804s) immicro=0.0984 imperial_eskd=0.0987
77
+ 2026-10-02T21:23:02 step 18750 train 0.09768 inner-val 0.09858 best 0.09858@18500 (815s) immicro=0.0984 imperial_eskd=0.0987
78
+ 2026-10-02T21:23:13 step 19000 train 0.09761 inner-val 0.09858 best 0.09858@18500 (826s) immicro=0.0984 imperial_eskd=0.0987
79
+ 2026-10-02T21:23:25 step 19250 train 0.09773 inner-val 0.09858 best 0.09858@18500 (838s) immicro=0.0984 imperial_eskd=0.0987
80
+ 2026-10-02T21:23:36 step 19500 train 0.09775 inner-val 0.09858 best 0.09858@18500 (849s) immicro=0.0984 imperial_eskd=0.0987
81
+ 2026-10-02T21:23:47 step 19750 train 0.09757 inner-val 0.09857 best 0.09857@19750 (860s) immicro=0.0984 imperial_eskd=0.0988
82
+ 2026-10-02T21:23:59 step 20000 train 0.09776 inner-val 0.09857 best 0.09857@19750 (872s) immicro=0.0984 imperial_eskd=0.0988
83
+ 2026-10-02T21:24:03 saved /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/ho_ye2026_sepsis/K1000/lora16/checkpoint_ft.pt (selected step 19750, inner-val 0.09857 vs pretrained 0.10749); sha a3fa8d637652
paper-releases/pbisc-diffusion/updates/20261002T124820Z-crosscohort-liu-default/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/AGGREGATE.md ADDED
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Cross-cohort (LOCO) benchmark: scores
2
+
3
+ Generated by `tools/aggregate_loco.py`; do not edit. Zero-pair arms first, then within-cohort references that used the held-out cohort's own pairs.
4
+
5
+ ## ImMicro (107 samples; reference c5e2547be3ec)
6
+
7
+ | arm | comp_l1 | p_l1_vs_zs_K1000 | precision | coverage | coverage_dm | pb_r_median |
8
+ |---|---|---|---|---|---|---|
9
+ | zs_K1000 | 0.446 | | 0.671 | 0.186 | 0.211 | 0.892 |
10
+ | [within-cohort calibration] zs__cal | 0.199 | 7.12e-17 | 0.905 | 0.527 | 0.769 | 0.88 |
11
+ | [within-cohort LoRA r16] real_lora__raw | 0.162 | 1.05e-18 | 0.924 | 0.784 | 0.962 | 0.899 |
12
+ | [within-cohort full FT] real_full__raw | 0.157 | 6e-19 | 0.89 | 0.695 | 0.95 | 0.914 |
13
+
14
+ ## Sepsis (82 samples; reference bac12da8a241)
15
+
16
+ | arm | comp_l1 | p_l1_vs_zs_K1000 | precision | coverage | coverage_dm | pb_r_median | logfc_r | broadcast_r | partial_r | deg50 | deg50_broadcast | deg50_max |
17
+ |---|---|---|---|---|---|---|---|---|---|---|---|---|
18
+ | loco_lora16_K1000 | 0.538 | 0.184 | 0.851 | 0.288 | 0.513 | 0.866 | 0.0916 | 0.316 | 0.0336 | 7 | 21 | 250 |
19
+ | zs_K1000 | 0.509 | | 0.617 | 0.074 | 0.0712 | 0.879 | -0.0218 | 0.316 | -0.103 | 9 | 21 | 250 |
20
+ | [within-cohort calibration] zs__cal | 0.401 | 0.000189 | 0.903 | 0.431 | 0.683 | 0.861 | 0.123 | 0.278 | 0.0394 | 11 | 22 | 250 |
21
+ | [within-cohort LoRA r16] real_lora__raw | 0.362 | 0.000685 | 0.926 | 0.829 | 0.949 | 0.891 | 0.527 | 0.316 | 0.469 | 23 | 21 | 250 |
22
+ | [within-cohort full FT] real_full__raw | 0.372 | 0.000402 | 0.866 | 0.795 | 0.927 | 0.901 | 0.534 | 0.316 | 0.468 | 24 | 21 | 250 |
23
+
24
+ ## Imperial (157 samples; reference 34b4caa3f8a6)
25
+
26
+ | arm | comp_l1 | p_l1_vs_zs_K1000 | precision | coverage | coverage_dm | pb_r_median | logfc_r | broadcast_r | partial_r | deg50 | deg50_broadcast | deg50_max |
27
+ |---|---|---|---|---|---|---|---|---|---|---|---|---|
28
+ | zs_K1000 | 0.803 | | 0.494 | 0.113 | 0.157 | 0.884 | 0.642 | 0.661 | 0.428 | 108 | 84 | 250 |
29
+ | [within-cohort calibration] zs__cal | 0.494 | 8.14e-16 | 0.842 | 0.639 | 0.688 | 0.867 | 0.679 | 0.69 | 0.412 | 111 | 97 | 250 |
30
+ | [within-cohort LoRA r16] real_lora__raw | 0.376 | 9.15e-21 | 0.847 | 0.85 | 0.937 | 0.88 | 0.819 | 0.661 | 0.69 | 143 | 84 | 250 |
31
+
32
+ ## Giroux (24 samples; reference dd0152f7bde4)
33
+
34
+ | arm | comp_l1 | p_l1_vs_zs_K1000 | precision | coverage | coverage_dm | pb_r_median | logfc_r | broadcast_r | partial_r | deg50 | deg50_broadcast | deg50_max |
35
+ |---|---|---|---|---|---|---|---|---|---|---|---|---|
36
+ | loco_lora16_K1000 | 0.347 | 0.833 | 0.991 | 0.808 | 0.826 | 0.847 | 0.296 | 0.575 | 0.188 | 0 | 5 | 101 |
37
+ | zs_K1000 | 0.337 | | 0.904 | 0.293 | 0.369 | 0.889 | 0.34 | 0.575 | 0.197 | 0 | 5 | 101 |
38
+ | [within-cohort calibration] zs__cal | 0.266 | 0.00958 | 0.995 | 0.906 | 0.902 | 0.865 | 0.269 | 0.564 | 0.133 | 0 | 2 | 101 |
39
+ | [within-cohort LoRA r16] real_lora__raw | 0.258 | 0.0315 | 0.998 | 0.928 | 0.974 | 0.865 | 0.461 | 0.575 | 0.323 | 5 | 5 | 101 |
40
+
41
+ ## Ji (whole blood) (33 samples; reference 19c30f56db69)
42
+
43
+ | arm | comp_l1 | p_l1_vs_zs_K1000 | precision | coverage | coverage_dm | pb_r_median | logfc_r | broadcast_r | partial_r | deg50 | deg50_broadcast | deg50_max |
44
+ |---|---|---|---|---|---|---|---|---|---|---|---|---|
45
+ | loco_lora16_K1000 | 0.494 | 0.000602 | 0.845 | 0.313 | 0.521 | 0.836 | 0.0892 | 0.24 | 0.0417 | 9 | 4 | 250 |
46
+ | zs_K1000 | 0.725 | | 0.721 | 0.059 | 0.195 | 0.834 | 0.0749 | 0.24 | 0.023 | 0 | 4 | 250 |
47
+ | [within-cohort calibration] zs__cal | 0.501 | 0.0011 | 0.74 | 0.106 | 0.347 | 0.839 | 0.142 | 0.206 | 0.113 | 0 | 1 | 250 |
48
+ | [within-cohort LoRA r16] real_lora__raw | 0.504 | 0.00879 | 0.971 | 0.857 | 0.965 | 0.873 | 0.479 | 0.24 | 0.452 | 15 | 4 | 250 |
49
+
paper-releases/pbisc-diffusion/updates/20261002T124820Z-crosscohort-liu-default/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/aggregate.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ cohort,arm,uses_target_pairs,comp_l1,precision,coverage,precision_dm,coverage_dm,pb_r_median,logfc_r,broadcast_r,partial_r,deg50,deg50_broadcast,deg50_max,p_l1_vs_zs_K1000,p_l1_vs_within_lora
2
+ immicro,zs_K1000,False,0.4457108278919341,0.6705919003115265,0.1864797507788162,0.6563239875389408,0.2107165109034268,0.8920390135033525,,,,,,,,1.0480605741268486e-18
3
+ immicro,[within-cohort calibration] zs__cal,True,0.19850275515696958,0.9050467289719626,0.527227414330218,0.944423676012461,0.7689719626168224,0.8801553120873381,,,,,,,7.12072465706631e-17,1.930319432195753e-07
4
+ immicro,[within-cohort LoRA r16] real_lora__raw,True,0.16172890350786212,0.9241121495327103,0.7842990654205607,0.9606230529595016,0.9623052959501558,0.8994149755898816,,,,,,,1.0480605741268486e-18,
5
+ immicro,[within-cohort full FT] real_full__raw,True,0.1572206245036138,0.8895327102803738,0.6953894080996885,0.9459190031152648,0.9501557632398754,0.9140523649103616,,,,,,,6.000732255664671e-19,0.24760598984759485
6
+ ye2026_sepsis,loco_lora16_K1000,False,0.537784206603567,0.8506504065040651,0.28804878048780486,0.7960162601626016,0.5129268292682927,0.8657510412987999,0.09162100245097674,0.31594581626202867,0.03357463198358491,7.0,21.0,250.0,0.18381713324066362,8.810964325972628e-07
7
+ ye2026_sepsis,zs_K1000,False,0.5094583598384997,0.6169918699186991,0.07398373983739838,0.5752032520325203,0.07121951219512195,0.8786516011139656,-0.021848803748335324,0.31594581626202867,-0.10302610190502556,9.0,21.0,250.0,,0.000684921410799816
8
+ ye2026_sepsis,[within-cohort calibration] zs__cal,True,0.40065324062398555,0.9034146341463415,0.4308130081300813,0.9110569105691056,0.6827642276422764,0.8611254347365214,0.12267970089558222,0.27791460488477016,0.03938214909552812,11.0,22.0,250.0,0.00018919827139893567,0.19313667138032242
9
+ ye2026_sepsis,[within-cohort LoRA r16] real_lora__raw,True,0.3618246783833512,0.9256910569105691,0.829349593495935,0.9334959349593496,0.9490243902439024,0.890814724779387,0.5267612711229187,0.31594581626202867,0.46874787884206276,23.0,21.0,250.0,0.000684921410799816,
10
+ ye2026_sepsis,[within-cohort full FT] real_full__raw,True,0.3716534409830164,0.8664227642276423,0.7946341463414635,0.8782926829268293,0.9273983739837398,0.9013444743771939,0.534366534546327,0.31594581626202867,0.46768653125846193,24.0,21.0,250.0,0.00040186525013656175,0.7132287608306445
11
+ imperial_eskd,zs_K1000,False,0.8029956289662181,0.4935695426283962,0.11315049825398177,0.5616642534707436,0.1567157823013372,0.8842598187000824,0.6419400899561818,0.6606053132085035,0.4277975641323528,108.0,84.0,250.0,,9.145080649116403e-21
12
+ imperial_eskd,[within-cohort calibration] zs__cal,True,0.4943429956255003,0.8417085427135679,0.6392981858444766,0.8640235073673452,0.6882292820032365,0.8669239796274295,0.6787028713891314,0.6901082987421676,0.41180392471305144,111.0,97.0,250.0,8.137216788076729e-16,7.01268509482979e-11
13
+ imperial_eskd,[within-cohort LoRA r16] real_lora__raw,True,0.37638560875745664,0.847457627118644,0.8500127757431224,0.8881270760582574,0.9371433438378333,0.8801149509776351,0.8187448084273437,0.6606053132085035,0.6899314750539278,143.0,84.0,250.0,9.145080649116403e-21,
14
+ giroux2022,loco_lora16_K1000,False,0.34714171317520415,0.9911111111111112,0.8077777777777778,0.9630555555555556,0.8263888888888888,0.8467677060411474,0.29602421545115876,0.5752659929360795,0.18828174123576252,0.0,5.0,101.0,0.8333734273910522,0.15998530387878418
15
+ giroux2022,zs_K1000,False,0.33717201742780784,0.9044444444444445,0.2927777777777778,0.9727777777777777,0.36944444444444446,0.889358629460119,0.33988455976970655,0.5752659929360795,0.1974453132347231,0.0,5.0,101.0,,0.03148162364959717
16
+ giroux2022,[within-cohort calibration] zs__cal,True,0.2663183045196587,0.9947222222222222,0.9061111111111111,0.9805555555555555,0.9019444444444444,0.865104686301811,0.26862025176520443,0.5643395657145305,0.13339193412452294,0.0,2.0,101.0,0.009575724601745605,0.7898378372192383
17
+ giroux2022,[within-cohort LoRA r16] real_lora__raw,True,0.2580442754044374,0.9980555555555556,0.9280555555555555,0.9841666666666666,0.9744444444444444,0.8654960479659805,0.46094848304079133,0.5752659929360795,0.3230347269696575,5.0,5.0,101.0,0.03148162364959717,
18
+ ji2025_chili,loco_lora16_K1000,False,0.4942971856123803,0.8452525252525253,0.31272727272727274,0.7476767676767677,0.5208080808080808,0.8363609394362492,0.08922685293161287,0.2398746224459366,0.04166517163260728,9.0,4.0,250.0,0.0006023156456649303,0.9578276048414409
19
+ ji2025_chili,zs_K1000,False,0.7246556382795132,0.7212121212121212,0.05898989898989899,0.4991919191919192,0.19474747474747475,0.8343238300989376,0.07494970415326974,0.2398746224459366,0.022996024886643866,0.0,4.0,250.0,,0.008788360049948096
20
+ ji2025_chili,[within-cohort calibration] zs__cal,True,0.5005691181619872,0.74,0.10646464646464647,0.6703030303030303,0.3472727272727273,0.8385862483484186,0.14191696206220386,0.20647363470913363,0.11277544159311834,0.0,1.0,250.0,0.0011035518255084753,0.9578276048414409
21
+ ji2025_chili,[within-cohort LoRA r16] real_lora__raw,True,0.5044992874562503,0.9709090909090909,0.8569696969696969,0.9727272727272728,0.9648484848484848,0.8728442542892082,0.47928798340225065,0.2398746224459366,0.45204372964017614,15.0,4.0,250.0,0.008788360049948096,