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Copy pathNEWER_OUTPUT_FROM_NEW_NAS_SCRIPT.txt
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NEWER_OUTPUT_FROM_NEW_NAS_SCRIPT.txt
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python3 new_nas_eval_script.py
TARGET: asset_index
FEATURES: ['target_sentence']
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, 0.8518518518518519, 0.5555555555555556, 0.5555555555555556, 0.5555555555555556, 0.5555555555555556, 0.5555555555555556, 0.5555555555555556, 0.5555555555555556, 0.5555555555555556]
basic neural network classifier fit time: 43.979s, roc auc: 0.73
precision recall f1-score support
0 0.82 0.86 0.84 268
1 0.66 0.60 0.63 122
accuracy 0.78 390
macro avg 0.74 0.73 0.74 390
weighted avg 0.77 0.78 0.78 390
CURRENT SCORE: 0.7301504771225839
TARGET: asset_index
FEATURES: ['document']
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, 0.3382663847780127, 0.3382663847780127, 0.3382663847780127, 0.3382663847780127, 0.3382663847780127]
basic neural network classifier fit time: 164.028s, 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 1077
1 0.38 1.00 0.55 665
accuracy 0.38 1742
macro avg 0.19 0.50 0.28 1742
weighted avg 0.15 0.38 0.21 1742
CURRENT SCORE: 0.5
TARGET: asset_index
FEATURES: ['target_sentence', 'document']
train data shape: (1960, 684), test data shape: (390, 684)
462 (23.57%) positive examples in train data, 122 (31.28%) positive examples in test data
[0.8532110091743119, 0.8532110091743119, 0.8532110091743119, 0.8532110091743119, 0.8532110091743119, 0.8532110091743119, 0.8532110091743119, 0.8532110091743119, 0.8532110091743119, 0.8532110091743119]
basic neural network classifier fit time: 45.532s, 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 0.69 1.00 0.81 268
1 0.00 0.00 0.00 122
accuracy 0.69 390
macro avg 0.34 0.50 0.41 390
weighted avg 0.47 0.69 0.56 390
CURRENT SCORE: 0.5
TARGET: sanitation_index
FEATURES: ['target_sentence']
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
[1.0, 1.0, 1.0, 1.0, 1.0, 0.8796992481203008, 0.7593984962406015, 0.8796992481203008, 0.7593984962406015, 0.8796992481203008]
basic neural network classifier fit time: 25.373s, roc auc: 0.673
precision recall f1-score support
0 0.63 0.85 0.73 293
1 0.76 0.50 0.60 286
accuracy 0.68 579
macro avg 0.70 0.67 0.66 579
weighted avg 0.70 0.68 0.66 579
CURRENT SCORE: 0.6732081911262797
TARGET: sanitation_index
FEATURES: ['document']
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
[0.6955602536997886, 0.6955602536997886, 0.5940803382663847, 0.6617336152219874, 0.5602536997885835, 0.6617336152219874, 0.6617336152219874, 0.5940803382663847, 0.627906976744186, 0.5264270613107822]
basic neural network classifier fit time: 202.761s, roc auc: 0.659
precision recall f1-score support
0 0.76 0.59 0.66 1131
1 0.55 0.73 0.63 792
accuracy 0.65 1923
macro avg 0.65 0.66 0.65 1923
weighted avg 0.67 0.65 0.65 1923
CURRENT SCORE: 0.6587610186748116
TARGET: sanitation_index
FEATURES: ['target_sentence', 'document']
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
[0.8796992481203008, 0.8796992481203008, 0.12030075187969924, 0.12030075187969924, 0.12030075187969924, 0.8796992481203008, 0.12030075187969924, 0.8796992481203008, 0.8796992481203008, 0.8796992481203008]
basic neural network classifier fit time: 104.706s, 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 0.00 293
1 0.49 1.00 0.66 286
accuracy 0.49 579
macro avg 0.25 0.50 0.33 579
weighted avg 0.24 0.49 0.33 579
CURRENT SCORE: 0.5
TARGET: water_index
FEATURES: ['target_sentence']
train data shape: (3700, 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: 77.435s, 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
TARGET: water_index
FEATURES: ['document']
train data shape: (11166, 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: 134.207s, 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
TARGET: water_index
FEATURES: ['target_sentence', 'document']
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: 53.239s, 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
TARGET: women_edu
FEATURES: ['target_sentence']
train data shape: (4678, 384), test data shape: (1162, 384)
1044 (22.32%) positive examples in train data, 523 (45.01%) positive examples in test data
[1.0, 1.0, 1.0, 1.0, 1.0, 0.9384615384615385, 0.9384615384615385, 0.9384615384615385, 0.9384615384615385, 0.9384615384615385]
basic neural network classifier fit time: 77.603s, roc auc: 0.654
precision recall f1-score support
0 0.95 0.33 0.49 639
1 0.54 0.98 0.70 523
accuracy 0.62 1162
macro avg 0.75 0.65 0.59 1162
weighted avg 0.77 0.62 0.58 1162
CURRENT SCORE: 0.6538029964362337
TARGET: women_edu
FEATURES: ['document']
train data shape: (11373, 300), test data shape: (3015, 300)
3249 (28.57%) positive examples in train data, 1038 (34.43%) positive examples in test data
[0.569620253164557, 0.569620253164557, 0.569620253164557, 0.569620253164557, 0.569620253164557, 0.569620253164557, 0.569620253164557, 0.569620253164557, 0.569620253164557, 0.569620253164557]
basic neural network classifier fit time: 177.069s, roc auc: 0.512
precision recall f1-score support
0 0.66 0.98 0.79 1977
1 0.53 0.04 0.08 1038
accuracy 0.66 3015
macro avg 0.60 0.51 0.43 3015
weighted avg 0.62 0.66 0.55 3015
CURRENT SCORE: 0.5115599626923493
TARGET: women_edu
FEATURES: ['target_sentence', 'document']
train data shape: (4689, 684), test data shape: (1162, 684)
1042 (22.22%) 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, 1.0, 1.0, 1.0, 1.0]
basic neural network classifier fit time: 139.09s, 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 0.00 639
1 0.45 1.00 0.62 523
accuracy 0.45 1162
macro avg 0.23 0.50 0.31 1162
weighted avg 0.20 0.45 0.28 1162
CURRENT SCORE: 0.5