SynCoTrain/synth_commands.txt at main · sasanamari/SynCoTrain · GitHub

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
# We have two cotraining series:

# | Iteration    | series A               | series B               |
# |--------------|------------------------|------------------------|
# | Iteration 0  | schnet0                | alignn0                |
# | Iteration 1  | coAlignn1              | coSchnet1              |
# | Iteration 2  | coSchnet2              | coAlignn2              |
# | Iteration 3  | coAlignn3              | coSchnet3              |


# Iteration 0, series A: schnet0
# PU Data Selection
syncotrainmp_data_selection --experiment schnet0

# PU Learning
nohup syncotrainmp_schnet_train --experiment schnet0 --gpu_id 0 > nohups/schnet0_synth_gpu0.log &

# PU Analysis
syncotrainmp_schnet_analyze --experiment schnet0


# Iteration 0, series B: alignn0
# PU Data Selection
syncotrainmp_data_selection --experiment alignn0

# PU Learning
nohup syncotrainmp_alignn_train --experiment alignn0 --gpu_id 0 > nohups/alignn0_synth_gpu0.log &

# PU Analysis
syncotrainmp_alignn_analyze --experiment alignn0


# Iteration 1, series A: coAlignn1
# PU Data Selection
syncotrainmp_data_selection --experiment coAlignn1

# PU Learning
nohup syncotrainmp_alignn_train --experiment coAlignn1 --gpu_id 0 > nohups/coAlignn1_synth_gpu0.log &

# PU Analysis
syncotrainmp_alignn_analyze --experiment coAlignn1


# Iteration 1, series B: coSchnet1
# PU Data Selection
syncotrainmp_data_selection --experiment coSchnet1

# PU Learning
nohup syncotrainmp_schnet_train --experiment coSchnet1 --gpu_id 0 > nohups/coSchnet1_synth_gpu0.log &

# PU Analysis
syncotrainmp_schnet_analyze --experiment coSchnet1


# Iteration 2, series A: coSchnet2
# PU Data Selection
syncotrainmp_data_selection --experiment coSchnet2

# PU Learning
nohup syncotrainmp_schnet_train --experiment coSchnet2 --gpu_id 0 > nohups/coSchnet2_synth_gpu0.log &

# PU Analysis
syncotrainmp_schnet_analyze --experiment coSchnet2


# Iteration 2, series B: coAlignn2
# PU Data Selection
syncotrainmp_data_selection --experiment coAlignn2

# PU Learning
nohup syncotrainmp_alignn_train --experiment coAlignn2  --gpu_id 0 > nohups/coAlignn2_synth_gpu0.log &

# PU Analysis
syncotrainmp_alignn_analyze --experiment coAlignn2


# Iteration 3, series A: coAlignn3
# PU Data Selection
syncotrainmp_data_selection --experiment coAlignn3

# PU Learning
nohup syncotrainmp_alignn_train --experiment coAlignn3  --gpu_id 0 > nohups/coAlignn3_synth_gpu0.log &

# PU Analysis
syncotrainmp_alignn_analyze --experiment coAlignn3


# Iteration 3, series B: coSchnet3
# PU Data Selection
syncotrainmp_data_selection --experiment coSchnet3

# PU Learning
nohup syncotrainmp_schnet_train --experiment coSchnet3  --gpu_id 0 > nohups/coSchnet3_synth_gpu0.log &

# PU Analysis
syncotrainmp_schnet_analyze --experiment coSchnet3

# -------------------------------------------------------------------------------------

# Preparing Data and Training the Synthesizability Predictor

# Producing labels
python schnet_pred/label_by_average.py

# Augmenting the labeled data
python schnet_pred/data_augment.py

# Training the predictor
python schnet_pred/train_schnet.py

# Predicting test results
python schnet_pred/predict_schnet.py

# Predicing results for schnet_pred/data/<your_crsytal_data>.pkl
python schnet_pred/predict_schnet.py --input_file <your_crsytal_data>