-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathrun.sh
62 lines (48 loc) · 7.11 KB
/
run.sh
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
python3 src/models/tuning.py --model logreg --scoring recall --num_trials 1000 --dataset original --early-stopping 100 --study-name logreg_original_recall
python3 src/models/tuning.py --model logreg --scoring f2 --num_trials 1000 --dataset original --early-stopping 100 --study-name logreg_original_f2
python3 src/models/tuning.py --model logreg --scoring roc_auc --num_trials 1000 --dataset original --early-stopping 100 --study-name logreg_original_roc_auc
python3 src/models/tuning.py --model logreg --scoring f1 --num_trials 1000 --dataset original --early-stopping 100 --study-name logreg_original_f1
python3 src/models/tuning.py --model logreg --scoring recall --num_trials 1000 --dataset rfecv --early-stopping 100 --study-name logreg_rfecv_recall
python3 src/models/tuning.py --model logreg --scoring f2 --num_trials 1000 --dataset rfecv --early-stopping 100 --study-name logreg_rfecv_f2
python3 src/models/tuning.py --model logreg --scoring roc_auc --num_trials 1000 --dataset rfecv --early-stopping 100 --study-name logreg_rfecv_roc_auc
python3 src/models/tuning.py --model logreg --scoring f1 --num_trials 1000 --dataset rfecv --early-stopping 100 --study-name logreg_rfecv_f1
python3 src/models/tuning.py --model logreg --scoring recall --num_trials 1000 --dataset pruned --early-stopping 100 --study-name logreg_pruned_recall
python3 src/models/tuning.py --model logreg --scoring f2 --num_trials 1000 --dataset pruned --early-stopping 100 --study-name logreg_pruned_f2
python3 src/models/tuning.py --model logreg --scoring roc_auc --num_trials 1000 --dataset pruned --early-stopping 100 --study-name logreg_pruned_roc_auc
python3 src/models/tuning.py --model logreg --scoring f1 --num_trials 1000 --dataset pruned --early-stopping 100 --study-name logreg_pruned_f1
python3 src/models/tuning.py --model logreg --scoring recall --num_trials 1000 --dataset top_35 --early-stopping 100 --study-name logreg_top_35_recall
python3 src/models/tuning.py --model logreg --scoring f2 --num_trials 1000 --dataset top_35 --early-stopping 100 --study-name logreg_top_35_f2
python3 src/models/tuning.py --model logreg --scoring roc_auc --num_trials 1000 --dataset top_35 --early-stopping 100 --study-name logreg_top_35_roc_auc
python3 src/models/tuning.py --model logreg --scoring f1 --num_trials 1000 --dataset top_35 --early-stopping 100 --study-name logreg_top_35_f1
python3 src/models/tuning.py --model random_forest --scoring recall --num_trials 1000 --dataset original --early-stopping 200 --study-name random_forest_original_recall
python3 src/models/tuning.py --model random_forest --scoring f2 --num_trials 1000 --dataset original --early-stopping 200 --study-name random_forest_original_f2
python3 src/models/tuning.py --model random_forest --scoring roc_auc --num_trials 1000 --dataset original --early-stopping 200 --study-name random_forest_original_roc_auc
python3 src/models/tuning.py --model random_forest --scoring f1 --num_trials 1000 --dataset original --early-stopping 200 --study-name random_forest_original_f1
python3 src/models/tuning.py --model random_forest --scoring recall --num_trials 1000 --dataset rfecv --early-stopping 200 --study-name random_forest_rfecv_recall
python3 src/models/tuning.py --model random_forest --scoring f2 --num_trials 1000 --dataset rfecv --early-stopping 200 --study-name random_forest_rfecv_f2
python3 src/models/tuning.py --model random_forest --scoring roc_auc --num_trials 1000 --dataset rfecv --early-stopping 200 --study-name random_forest_rfecv_roc_auc
python3 src/models/tuning.py --model random_forest --scoring f1 --num_trials 1000 --dataset rfecv --early-stopping 200 --study-name random_forest_rfecv_f1
python3 src/models/tuning.py --model random_forest --scoring recall --num_trials 1000 --dataset pruned --early-stopping 200 --study-name random_forest_pruned_recall
python3 src/models/tuning.py --model random_forest --scoring f2 --num_trials 1000 --dataset pruned --early-stopping 200 --study-name random_forest_pruned_f2
python3 src/models/tuning.py --model random_forest --scoring roc_auc --num_trials 1000 --dataset pruned --early-stopping 200 --study-name random_forest_pruned_roc_auc
python3 src/models/tuning.py --model random_forest --scoring f1 --num_trials 1000 --dataset pruned --early-stopping 200 --study-name random_forest_pruned_f1
python3 src/models/tuning.py --model random_forest --scoring recall --num_trials 1000 --dataset top_35 --early-stopping 200 --study-name random_forest_top_35_recall
python3 src/models/tuning.py --model random_forest --scoring f2 --num_trials 1000 --dataset top_35 --early-stopping 200 --study-name random_forest_top_35_f2
python3 src/models/tuning.py --model random_forest --scoring roc_auc --num_trials 1000 --dataset top_35 --early-stopping 200 --study-name random_forest_top_35_roc_auc
python3 src/models/tuning.py --model random_forest --scoring f1 --num_trials 1000 --dataset top_35 --early-stopping 200 --study-name random_forest_top_35_f1
python3 src/models/tuning.py --model lgbm --scoring recall --num_trials 1000 --dataset original --early-stopping 200 --study-name lgbm_original_recall
python3 src/models/tuning.py --model lgbm --scoring f2 --num_trials 1000 --dataset original --early-stopping 200 --study-name lgbm_original_f2
python3 src/models/tuning.py --model lgbm --scoring roc_auc --num_trials 1000 --dataset original --early-stopping 200 --study-name lgbm_original_roc_auc
python3 src/models/tuning.py --model lgbm --scoring f1 --num_trials 1000 --dataset original --early-stopping 200 --study-name lgbm_original_f1
python3 src/models/tuning.py --model lgbm --scoring recall --num_trials 1000 --dataset rfecv --early-stopping 200 --study-name lgbm_rfecv_recall
python3 src/models/tuning.py --model lgbm --scoring f2 --num_trials 1000 --dataset rfecv --early-stopping 200 --study-name lgbm_rfecv_f2
python3 src/models/tuning.py --model lgbm --scoring roc_auc --num_trials 1000 --dataset rfecv --early-stopping 200 --study-name lgbm_rfecv_roc_auc
python3 src/models/tuning.py --model lgbm --scoring f1 --num_trials 1000 --dataset rfecv --early-stopping 200 --study-name lgbm_rfecv_f1
python3 src/models/tuning.py --model lgbm --scoring recall --num_trials 1000 --dataset pruned --early-stopping 200 --study-name lgbm_pruned_recall
python3 src/models/tuning.py --model lgbm --scoring f2 --num_trials 1000 --dataset pruned --early-stopping 200 --study-name lgbm_pruned_f2
python3 src/models/tuning.py --model lgbm --scoring roc_auc --num_trials 1000 --dataset pruned --early-stopping 200 --study-name lgbm_pruned_roc_auc
python3 src/models/tuning.py --model lgbm --scoring f1 --num_trials 1000 --dataset pruned --early-stopping 200 --study-name lgbm_pruned_f1
python3 src/models/tuning.py --model lgbm --scoring recall --num_trials 1000 --dataset top_35 --early-stopping 200 --study-name lgbm_top_35_recall
python3 src/models/tuning.py --model lgbm --scoring f2 --num_trials 1000 --dataset top_35 --early-stopping 200 --study-name lgbm_top_35_f2
python3 src/models/tuning.py --model lgbm --scoring roc_auc --num_trials 1000 --dataset top_35 --early-stopping 200 --study-name lgbm_top_35_roc_auc
python3 src/models/tuning.py --model lgbm --scoring f1 --num_trials 1000 --dataset top_35 --early-stopping 200 --study-name lgbm_top_35_f1