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Francisco Rivera Valverde: [FIX] Enables backend to track the num run (#162)
1 parent 1cf2aa3 commit c55e151

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‎refactor_development/_downloads/2f0f54a9831653ac5c12ee0e06324a62/basics_tabular_jupyter.zip

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‎refactor_development/_downloads/8ef6602cf8ed40221edd89244fb031af/basics_tabular_python.zip

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‎refactor_development/_downloads/b38295cf56f02b1c4547385a8f389f90/advanced_tabular_python.zip

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‎refactor_development/_downloads/df038041811db9bd567de83692a8f994/advanced_tabular_jupyter.zip

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‎refactor_development/_sources/advanced_tabular/example_custom_configuration_space.rst.txt

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‎refactor_development/_sources/advanced_tabular/example_resampling_strategy.rst.txt

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‎refactor_development/_sources/advanced_tabular/sg_execution_times.rst.txt

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Computation times
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=================
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**21:42.119** total execution time for **advanced_tabular** files:
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**21:51.748** total execution time for **advanced_tabular** files:
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+--------------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_advanced_tabular_example_custom_configuration_space.py` (``example_custom_configuration_space.py``) | 11:43.290 | 0.0 MB |
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| :ref:`sphx_glr_advanced_tabular_example_custom_configuration_space.py` (``example_custom_configuration_space.py``) | 11:36.455 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_advanced_tabular_example_resampling_strategy.py` (``example_resampling_strategy.py``) | 09:58.829 | 0.0 MB |
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| :ref:`sphx_glr_advanced_tabular_example_resampling_strategy.py` (``example_resampling_strategy.py``) | 10:15.293 | 0.0 MB |
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‎refactor_development/_sources/basics_tabular/example_tabular_classification.rst.txt

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@@ -36,7 +36,7 @@ with AutoPyTorch
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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7f749966b6a0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7f7fcd09e700> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -68,7 +68,7 @@ with AutoPyTorch
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scaler:__choice__, Value: 'StandardScaler'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.002102375030517578, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.001974344253540039, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -100,7 +100,7 @@ with AutoPyTorch
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scaler:__choice__, Value: 'StandardScaler'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=1, ta_time_used=5.201563596725464, wallclock_time=6.676171779632568, budget=5.555555555555555), TrajEntry(train_perf=0.14035087719298245, incumbent_id=2, incumbent=Configuration:
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, ta_runs=1, ta_time_used=5.617596387863159, wallclock_time=7.137464761734009, budget=5.555555555555555), TrajEntry(train_perf=0.16374269005847952, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 153
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:KernelPCA:gamma, Value: 0.6217858094449208
@@ -133,21 +133,19 @@ with AutoPyTorch
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trainer:MixUpTrainer:alpha, Value: 0.7490557199071863
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trainer:MixUpTrainer:weighted_loss, Value: False
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trainer:__choice__, Value: 'MixUpTrainer'
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, ta_runs=4, ta_time_used=50.87985134124756, wallclock_time=58.71370840072632, budget=5.555555555555555)]
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{'accuracy': 0.8786127167630058}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.24 |
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| 1 | None | CatBoostClassifier | 0.18 |
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| 2 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
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| 3 | None | KNNClassifier | 0.1 |
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| 4 | SimpleImputer,NoEncoder,NoScaler,PolynomialFeatures | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 5 | None | ExtraTreesClassifier | 0.08 |
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| 6 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 8 | SimpleImputer,OneHotEncoder,MinMaxScaler,PolynomialFeatures | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 9 | SimpleImputer,NoEncoder,NoScaler,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 10 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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, ta_runs=4, ta_time_used=49.19730734825134, wallclock_time=57.43541860580444, budget=5.555555555555555)]
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{'accuracy': 0.8728323699421965}
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| | Preprocessing | Estimator | Weight |
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|---:|:-----------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | None | CatBoostClassifier | 0.36 |
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| 1 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 2 | None | RFClassifier | 0.1 |
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| 3 | None | ExtraTreesClassifier | 0.1 |
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| 4 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 5 | SimpleImputer,NoEncoder,NoScaler,PolynomialFeatures | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 6 | None | SVC | 0.06 |
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| 7 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 8 | None | KNNClassifier | 0.04 |
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 6 minutes 3.800 seconds)
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**Total running time of the script:** ( 6 minutes 5.257 seconds)
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.. _sphx_glr_download_basics_tabular_example_tabular_classification.py:

‎refactor_development/_sources/basics_tabular/example_tabular_regression.rst.txt

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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7f74912b30a0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7f7fc508cf40> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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imputer:categorical_strategy, Value: 'most_frequent'
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optimizer:__choice__, Value: 'AdamOptimizer'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0016345977783203125, budget=0), TrajEntry(train_perf=0.6495266348731004, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018508434295654297, budget=0), TrajEntry(train_perf=0.3455457959161278, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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imputer:categorical_strategy, Value: 'most_frequent'
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optimizer:__choice__, Value: 'AdamOptimizer'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=1, ta_time_used=4.129133701324463, wallclock_time=7.758663892745972, budget=5.555555555555555)]
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{'r2': 0.8833727208468919}
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, ta_runs=1, ta_time_used=4.5675368309021, wallclock_time=8.493228435516357, budget=5.555555555555555)]
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{'r2': 0.8982766406312925}
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| | Preprocessing | Estimator | Weight |
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|---:|:-------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,StandardScaler | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.7 |
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| 1 | SimpleImputer,OneHotEncoder,StandardScaler | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.3 |
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| 0 | SimpleImputer,OneHotEncoder,StandardScaler | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.78 |
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| 1 | SimpleImputer,OneHotEncoder,StandardScaler | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 2 | SimpleImputer,OneHotEncoder,StandardScaler | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 23.292 seconds)
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**Total running time of the script:** ( 5 minutes 13.817 seconds)
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.. _sphx_glr_download_basics_tabular_example_tabular_regression.py:

‎refactor_development/_sources/basics_tabular/sg_execution_times.rst.txt

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Computation times
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=================
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**11:27.092** total execution time for **basics_tabular** files:
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**11:19.074** total execution time for **basics_tabular** files:
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+----------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_basics_tabular_example_tabular_classification.py` (``example_tabular_classification.py``) | 06:03.800 | 0.0 MB |
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| :ref:`sphx_glr_basics_tabular_example_tabular_classification.py` (``example_tabular_classification.py``) | 06:05.257 | 0.0 MB |
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| :ref:`sphx_glr_basics_tabular_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:23.292 | 0.0 MB |
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| :ref:`sphx_glr_basics_tabular_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:13.817 | 0.0 MB |
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