-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathresults_rdst_inverted_all_2nd_rnd.txt
128 lines (123 loc) · 10.3 KB
/
results_rdst_inverted_all_2nd_rnd.txt
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
nohup: ignoring input
/home/carolina/Documents/Mestrado/.venv/lib/python3.9/site-packages/torch/cuda/__init__.py:141: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 11020). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
return torch._C._cuda_getDeviceCount() > 0
/home/carolina/Documents/Mestrado/.venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/home/carolina/Documents/Mestrado/.venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/home/carolina/Documents/Mestrado/.venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/home/carolina/Documents/Mestrado/.venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/home/carolina/Documents/Mestrado/.venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/home/carolina/Documents/Mestrado/.venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
Traceback (most recent call last):
File "/usr/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/carolina/Documents/Mestrado/classifiers/loop_locally.py", line 153, in <module>
create_tasks(K) # This one trains models with full dataset.
File "/home/carolina/Documents/Mestrado/classifiers/loop_locally.py", line 53, in create_tasks
values = [
File "/home/carolina/Documents/Mestrado/classifiers/loop_locally.py", line 54, in <listcomp>
results[metric] for results in kfold_results
TypeError: tuple indices must be integers or slices, not str
GPU is not available
Start time: 2024-01-12_22-27-10
timeseries_f1-5 (10163, 24) (10163,) (2541, 24) (2541,)
Loaded: timeseries_f1-5
id h00 h01 h02 ... h23 country city category
0 0 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
1 1 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
2 2 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
3 3 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
4 4 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ...
4481 4481 0.000000 0.0 0.0 ... 0.490978 0.0 2.0 4.0
4482 4482 0.000000 0.0 0.0 ... 0.000000 0.0 2.0 4.0
4483 4483 0.268889 0.0 0.0 ... 0.184940 0.0 2.0 4.0
4484 4484 0.000000 0.0 0.0 ... 0.000000 0.0 2.0 4.0
4485 4485 0.000000 0.0 0.0 ... 0.178723 0.0 2.0 4.0
[4486 rows x 28 columns]
Treinando no país 0 e testando no país 1
Treinando no país 1 e testando no país 0
<function run_rdst at 0x7efb3444c160>_f1-5 ({'accuracy_score': 0.14565587734241908, 'f1_score': 0.11212802362170103, 'precision_score': 0.37743249235716797, 'recall_score': 0.14565587734241908}, {'accuracy_score': 0.30472581364244317, 'f1_score': 0.18971103583036059, 'precision_score': 0.15898328684819052, 'recall_score': 0.30472581364244317})
timeseries_f2-5 (10163, 24) (10163,) (2541, 24) (2541,)
Loaded: timeseries_f2-5
id h00 h01 h02 ... h23 country city category
0 0 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
1 1 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
2 2 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
3 3 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
4 4 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ...
4481 4481 0.000000 0.0 0.0 ... 0.490978 0.0 2.0 4.0
4482 4482 0.000000 0.0 0.0 ... 0.000000 0.0 2.0 4.0
4483 4483 0.268889 0.0 0.0 ... 0.184940 0.0 2.0 4.0
4484 4484 0.000000 0.0 0.0 ... 0.000000 0.0 2.0 4.0
4485 4485 0.000000 0.0 0.0 ... 0.178723 0.0 2.0 4.0
[4486 rows x 28 columns]
Treinando no país 0 e testando no país 1
Treinando no país 1 e testando no país 0
<function run_rdst at 0x7efb3444c160>_f2-5 ({'accuracy_score': 0.1645169140910197, 'f1_score': 0.14132101628646782, 'precision_score': 0.15992606753481112, 'recall_score': 0.1645169140910197}, {'accuracy_score': 0.2336156932679447, 'f1_score': 0.13701521204969044, 'precision_score': 0.617200128319444, 'recall_score': 0.2336156932679447})
timeseries_f3-5 (10163, 24) (10163,) (2541, 24) (2541,)
Loaded: timeseries_f3-5
id h00 h01 h02 ... h23 country city category
0 0 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
1 1 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
2 2 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
3 3 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
4 4 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ...
4481 4481 0.000000 0.0 0.0 ... 0.490978 0.0 2.0 4.0
4482 4482 0.000000 0.0 0.0 ... 0.000000 0.0 2.0 4.0
4483 4483 0.268889 0.0 0.0 ... 0.184940 0.0 2.0 4.0
4484 4484 0.000000 0.0 0.0 ... 0.000000 0.0 2.0 4.0
4485 4485 0.000000 0.0 0.0 ... 0.178723 0.0 2.0 4.0
[4486 rows x 28 columns]
Treinando no país 0 e testando no país 1
Treinando no país 1 e testando no país 0
<function run_rdst at 0x7efb3444c160>_f3-5 ({'accuracy_score': 0.1271598929179849, 'f1_score': 0.10372376377229585, 'precision_score': 0.15420921902381168, 'recall_score': 0.1271598929179849}, {'accuracy_score': 0.2978154257690593, 'f1_score': 0.18879299500596056, 'precision_score': 0.16368143203091698, 'recall_score': 0.2978154257690593})
timeseries_f4-5 (10163, 24) (10163,) (2541, 24) (2541,)
Loaded: timeseries_f4-5
id h00 h01 h02 ... h23 country city category
0 0 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
1 1 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
2 2 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
3 3 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
4 4 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ...
4481 4481 0.000000 0.0 0.0 ... 0.490978 0.0 2.0 4.0
4482 4482 0.000000 0.0 0.0 ... 0.000000 0.0 2.0 4.0
4483 4483 0.268889 0.0 0.0 ... 0.184940 0.0 2.0 4.0
4484 4484 0.000000 0.0 0.0 ... 0.000000 0.0 2.0 4.0
4485 4485 0.000000 0.0 0.0 ... 0.178723 0.0 2.0 4.0
[4486 rows x 28 columns]
Treinando no país 0 e testando no país 1
Treinando no país 1 e testando no país 0
<function run_rdst at 0x7efb3444c160>_f4-5 ({'accuracy_score': 0.15271355560963737, 'f1_score': 0.11788199892066412, 'precision_score': 0.3771425963348197, 'recall_score': 0.15271355560963737}, {'accuracy_score': 0.251226036558181, 'f1_score': 0.1542129951845954, 'precision_score': 0.15934999817393808, 'recall_score': 0.251226036558181})
timeseries_f5-5 (10164, 24) (10164,) (2540, 24) (2540,)
Loaded: timeseries_f5-5
id h00 h01 h02 ... h23 country city category
0 0 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
1 1 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
2 2 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
3 3 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
4 4 0.000000 0.0 0.0 ... 0.000000 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ...
4481 4481 0.000000 0.0 0.0 ... 0.490978 0.0 2.0 4.0
4482 4482 0.000000 0.0 0.0 ... 0.000000 0.0 2.0 4.0
4483 4483 0.268889 0.0 0.0 ... 0.184940 0.0 2.0 4.0
4484 4484 0.000000 0.0 0.0 ... 0.000000 0.0 2.0 4.0
4485 4485 0.000000 0.0 0.0 ... 0.178723 0.0 2.0 4.0
[4486 rows x 28 columns]
Treinando no país 0 e testando no país 1
Treinando no país 1 e testando no país 0
<function run_rdst at 0x7efb3444c160>_f5-5 ({'accuracy_score': 0.10452664881966416, 'f1_score': 0.07624783303458688, 'precision_score': 0.1678106310887628, 'recall_score': 0.10452664881966416}, {'accuracy_score': 0.2893446277307178, 'f1_score': 0.18079743446097013, 'precision_score': 0.1616159211599518, 'recall_score': 0.2893446277307178})
<function run_rdst at 0x7efb3444c160>
Command exited with non-zero status 1
6276.19user 10523.05system 36:05.83elapsed 775%CPU (0avgtext+0avgdata 8044732maxresident)k
5176inputs+88outputs (0major+14709205minor)pagefaults 0swaps