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OLD_OUTPUT_FROM_NEW_NAS_SCRIPT.txt
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python3 new_nas_eval_script.py
train data shape: (1944, 384), test data shape: (390, 384)
462 (23.77%) positive examples in train data, 122 (31.28%) positive examples in test data
[0.5555555555555556, 0.4074074074074074, 0.5555555555555556, 0.5555555555555556, 0.5555555555555556, 0.5555555555555556, 0.5555555555555556, 0.2962962962962963, 0.5555555555555556, 0.5555555555555556]
basic neural network classifier fit time: 53.607s, roc auc: 0.725
precision recall f1-score support
0 0.82 0.85 0.84 268
1 0.65 0.60 0.62 122
accuracy 0.77 390
macro avg 0.73 0.72 0.73 390
weighted avg 0.77 0.77 0.77 390
CURRENT SCORE: 0.7245534621972107
No need to resample, classes are balanced within our tolerated class ratio of 0.3
train data shape: (8505, 300), test data shape: (1742, 300)
2706 (31.82%) positive examples in train data, 665 (38.17%) positive examples in test data
[0.3382663847780127, 0.3382663847780127, 0.3382663847780127, 0.3382663847780127, 0.3382663847780127, 0.7632135306553911, 0.7970401691331924, 0.7632135306553911, 0.7632135306553911, 0.7632135306553911]
basic neural network classifier fit time: 296.139s, roc auc: 0.712
precision recall f1-score support
0 0.83 0.63 0.72 1077
1 0.57 0.79 0.66 665
accuracy 0.69 1742
macro avg 0.70 0.71 0.69 1742
weighted avg 0.73 0.69 0.70 1742
CURRENT SCORE: 0.71182133606998
train data shape: (1959, 684), test data shape: (390, 684)
462 (23.58%) positive examples in train data, 122 (31.28%) positive examples in test data
[0.8532110091743119, 0.5596330275229358, 0.5596330275229358, 0.7064220183486238, 0.26605504587155965, 0.41284403669724773, 0.41284403669724773, 0.26605504587155965, 0.5596330275229358, 0.41284403669724773]
basic neural network classifier fit time: 77.475s, roc auc: 0.703
precision recall f1-score support
0 0.84 0.70 0.76 268
1 0.52 0.70 0.60 122
accuracy 0.70 390
macro avg 0.68 0.70 0.68 390
weighted avg 0.74 0.70 0.71 390
CURRENT SCORE: 0.7032052850501591
No need to resample, classes are balanced within our tolerated class ratio of 0.3
train data shape: (2394, 384), test data shape: (579, 384)
722 (30.16%) positive examples in train data, 286 (49.4%) positive examples in test data
[0.8796992481203008, 0.8796992481203008, 0.8796992481203008, 0.8796992481203008, 0.8796992481203008, 0.8796992481203008, 0.8796992481203008, 0.7593984962406015, 0.8796992481203008, 0.8796992481203008]
basic neural network classifier fit time: 105.584s, roc auc: 0.696
precision recall f1-score support
0 0.66 0.84 0.74 293
1 0.77 0.56 0.65 286
accuracy 0.70 579
macro avg 0.71 0.70 0.69 579
weighted avg 0.71 0.70 0.69 579
CURRENT SCORE: 0.6960607651733931
No need to resample, classes are balanced within our tolerated class ratio of 0.3
train data shape: (8505, 300), test data shape: (1923, 300)
2885 (33.92%) positive examples in train data, 792 (41.19%) positive examples in test data
[0.37209302325581395, 0.37209302325581395, 0.37209302325581395, 0.5750528541226215, 0.7293868921775899, 0.5940803382663847, 0.37209302325581395, 0.37209302325581395, 0.37209302325581395, 0.627906976744186]
basic neural network classifier fit time: 764.104s, roc auc: 0.5
precision recall f1-score support
0 0.59 1.00 0.74 1131
1 0.00 0.00 0.00 792
accuracy 0.59 1923
macro avg 0.29 0.50 0.37 1923
weighted avg 0.35 0.59 0.44 1923
CURRENT SCORE: 0.4995579133510168
No need to resample, classes are balanced within our tolerated class ratio of 0.3
train data shape: (2394, 684), test data shape: (579, 684)
722 (30.16%) positive examples in train data, 286 (49.4%) positive examples in test data
[0.12030075187969924, 0.12030075187969924, 0.12030075187969924, 0.12030075187969924, 0.12030075187969924, 0.12030075187969924, 0.12030075187969924, 0.12030075187969924, 0.12030075187969924, 0.12030075187969924]
basic neural network classifier fit time: 630.211s, roc auc: 0.5
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
precision recall f1-score support
0 0.51 1.00 0.67 293
1 0.00 0.00 0.00 286
accuracy 0.51 579
macro avg 0.25 0.50 0.34 579
weighted avg 0.26 0.51 0.34 579
CURRENT SCORE: 0.5
train data shape: (3699, 384), test data shape: (614, 384)
822 (22.22%) positive examples in train data, 163 (26.55%) positive examples in test data
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/init.py:388: UserWarning: Initializing zero-element tensors is a no-op
warnings.warn("Initializing zero-element tensors is a no-op")
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
basic neural network classifier fit time: 59.898s, roc auc: 0.5
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
precision recall f1-score support
0 0.00 0.00 0.00 451
1 0.27 1.00 0.42 163
accuracy 0.27 614
macro avg 0.13 0.50 0.21 614
weighted avg 0.07 0.27 0.11 614
CURRENT SCORE: 0.5
train data shape: (11164, 300), test data shape: (1742, 300)
2481 (22.22%) positive examples in train data, 392 (22.5%) positive examples in test data
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
basic neural network classifier fit time: 110.544s, roc auc: 0.5
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
precision recall f1-score support
0 0.00 0.00 0.00 1350
1 0.23 1.00 0.37 392
accuracy 0.23 1742
macro avg 0.11 0.50 0.18 1742
weighted avg 0.05 0.23 0.08 1742
CURRENT SCORE: 0.5
train data shape: (3703, 684), test data shape: (614, 684)
822 (22.2%) positive examples in train data, 163 (26.55%) positive examples in test data
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/init.py:388: UserWarning: Initializing zero-element tensors is a no-op
warnings.warn("Initializing zero-element tensors is a no-op")
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
basic neural network classifier fit time: 41.822s, roc auc: 0.5
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are 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))
precision recall f1-score support
0 0.73 1.00 0.85 451
1 0.00 0.00 0.00 163
accuracy 0.73 614
macro avg 0.37 0.50 0.42 614
weighted avg 0.54 0.73 0.62 614
CURRENT SCORE: 0.5
train data shape: (4670, 384), test data shape: (1162, 384)
1045 (22.38%) positive examples in train data, 523 (45.01%) positive examples in test data
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9865125240847784, 0.9865125240847784, 0.9865125240847784, 0.9865125240847784]
Traceback (most recent call last):
File "new_nas_eval_script.py", line 169, in <module>
graph_data[i][j] = acc_vals
IndexError: list index out of range