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Francisco Rivera Valverde: [Fix] long running regression (#272)
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23 changes: 10 additions & 13 deletions development/_sources/examples/20_basics/example_image_classification.rst.txt
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Expand Up @@ -85,27 +85,24 @@ Image Classification
Pipeline Random Config:
________________________________________
Configuration:
image_augmenter:GaussianBlur:sigma_min, Value: 0.20908525787627186
image_augmenter:GaussianBlur:sigma_offset, Value: 1.0594983119215948
image_augmenter:GaussianBlur:use_augmenter, Value: True
image_augmenter:GaussianNoise:sigma_offset, Value: 2.332578136892134
image_augmenter:GaussianNoise:use_augmenter, Value: True
image_augmenter:RandomAffine:rotate, Value: 304
image_augmenter:RandomAffine:scale_offset, Value: 0.11742982276455081
image_augmenter:RandomAffine:shear, Value: 29
image_augmenter:RandomAffine:translate_percent_offset, Value: 0.2818176238920318
image_augmenter:GaussianBlur:use_augmenter, Value: False
image_augmenter:GaussianNoise:use_augmenter, Value: False
image_augmenter:RandomAffine:rotate, Value: 62
image_augmenter:RandomAffine:scale_offset, Value: 0.21821106872525045
image_augmenter:RandomAffine:shear, Value: 27
image_augmenter:RandomAffine:translate_percent_offset, Value: 0.2725441553411913
image_augmenter:RandomAffine:use_augmenter, Value: True
image_augmenter:RandomCutout:use_augmenter, Value: False
image_augmenter:Resize:use_augmenter, Value: False
image_augmenter:ZeroPadAndCrop:percent, Value: 0.08548217815014147
normalizer:__choice__, Value: 'ImageNormalizer'
image_augmenter:ZeroPadAndCrop:percent, Value: 0.01843399163439513
normalizer:__choice__, Value: 'NoNormalizer'

Fitting the pipeline...
________________________________________
ImageClassificationPipeline
________________________________________
0-) normalizer:
ImageNormalizer
NoNormalizer

1-) preprocessing:
EarlyPreprocessing
Expand Down Expand Up @@ -177,7 +174,7 @@ Image Classification
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 8.553 seconds)
**Total running time of the script:** ( 0 minutes 7.788 seconds)


.. _sphx_glr_download_examples_20_basics_example_image_classification.py:
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14 changes: 7 additions & 7 deletions development/_sources/examples/20_basics/example_tabular_classification.rst.txt
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Expand Up @@ -133,7 +133,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f8a02b64c10>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f4834490160>
Expand Down Expand Up @@ -162,7 +162,7 @@ Print the final ensemble performance

.. code-block:: none
<smac.runhistory.runhistory.RunHistory object at 0x7f89f18b7910> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
<smac.runhistory.runhistory.RunHistory object at 0x7f4834490f10> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
data_loader:batch_size, Value: 64
encoder:__choice__, Value: 'OneHotEncoder'
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
Expand Down Expand Up @@ -194,7 +194,7 @@ Print the final ensemble performance
scaler:__choice__, Value: 'StandardScaler'
trainer:StandardTrainer:weighted_loss, Value: True
trainer:__choice__, Value: 'StandardTrainer'
, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0012984275817871094, budget=0), TrajEntry(train_perf=0.18128654970760238, incumbent_id=1, incumbent=Configuration:
, ta_runs=0, ta_time_used=0.0, wallclock_time=0.001138925552368164, budget=0), TrajEntry(train_perf=0.18128654970760238, incumbent_id=1, incumbent=Configuration:
data_loader:batch_size, Value: 64
encoder:__choice__, Value: 'OneHotEncoder'
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
Expand Down Expand Up @@ -226,7 +226,7 @@ Print the final ensemble performance
scaler:__choice__, Value: 'StandardScaler'
trainer:StandardTrainer:weighted_loss, Value: True
trainer:__choice__, Value: 'StandardTrainer'
, ta_runs=1, ta_time_used=2.5365962982177734, wallclock_time=3.641376495361328, budget=5.555555555555555), TrajEntry(train_perf=0.17543859649122806, incumbent_id=2, incumbent=Configuration:
, ta_runs=1, ta_time_used=2.615473747253418, wallclock_time=3.7216246128082275, budget=5.555555555555555), TrajEntry(train_perf=0.17543859649122806, incumbent_id=2, incumbent=Configuration:
data_loader:batch_size, Value: 97
encoder:__choice__, Value: 'OneHotEncoder'
feature_preprocessor:KernelPCA:coef0, Value: -0.082773840425306
Expand Down Expand Up @@ -264,7 +264,7 @@ Print the final ensemble performance
scaler:__choice__, Value: 'MinMaxScaler'
trainer:StandardTrainer:weighted_loss, Value: False
trainer:__choice__, Value: 'StandardTrainer'
, ta_runs=3, ta_time_used=18.902961254119873, wallclock_time=22.37511134147644, budget=5.555555555555555), TrajEntry(train_perf=0.18128654970760238, incumbent_id=3, incumbent=Configuration:
, ta_runs=3, ta_time_used=19.759934902191162, wallclock_time=23.234455108642578, budget=5.555555555555555), TrajEntry(train_perf=0.18128654970760238, incumbent_id=3, incumbent=Configuration:
data_loader:batch_size, Value: 64
encoder:__choice__, Value: 'OneHotEncoder'
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
Expand Down Expand Up @@ -296,7 +296,7 @@ Print the final ensemble performance
scaler:__choice__, Value: 'StandardScaler'
trainer:StandardTrainer:weighted_loss, Value: True
trainer:__choice__, Value: 'StandardTrainer'
, ta_runs=11, ta_time_used=83.38277745246887, wallclock_time=98.77164840698242, budget=16.666666666666664)]
, ta_runs=11, ta_time_used=88.6544451713562, wallclock_time=104.4312117099762, budget=16.666666666666664)]
{'accuracy': 0.8728323699421965}
| | Preprocessing | Estimator | Weight |
|---:|:---------------------------------------------------|:-------------------------------------------------------------------|---------:|
Expand All @@ -313,7 +313,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 35.924 seconds)
**Total running time of the script:** ( 5 minutes 37.322 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:
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16 changes: 8 additions & 8 deletions development/_sources/examples/20_basics/example_tabular_regression.rst.txt
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Expand Up @@ -125,7 +125,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f8aaace4880>
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f48dfd18d00>
Expand Down Expand Up @@ -157,7 +157,7 @@ Print the final ensemble performance

.. code-block:: none
<smac.runhistory.runhistory.RunHistory object at 0x7f8aaada68b0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
<smac.runhistory.runhistory.RunHistory object at 0x7f48e345cbe0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
data_loader:batch_size, Value: 64
encoder:__choice__, Value: 'OneHotEncoder'
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
Expand Down Expand Up @@ -188,7 +188,7 @@ Print the final ensemble performance
optimizer:__choice__, Value: 'AdamOptimizer'
scaler:__choice__, Value: 'StandardScaler'
trainer:__choice__, Value: 'StandardTrainer'
, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0014433860778808594, budget=0), TrajEntry(train_perf=4.940860613424574, incumbent_id=1, incumbent=Configuration:
, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0013155937194824219, budget=0), TrajEntry(train_perf=4.940860613424574, incumbent_id=1, incumbent=Configuration:
data_loader:batch_size, Value: 64
encoder:__choice__, Value: 'OneHotEncoder'
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
Expand Down Expand Up @@ -219,7 +219,7 @@ Print the final ensemble performance
optimizer:__choice__, Value: 'AdamOptimizer'
scaler:__choice__, Value: 'StandardScaler'
trainer:__choice__, Value: 'StandardTrainer'
, ta_runs=1, ta_time_used=1.7743275165557861, wallclock_time=3.002695322036743, budget=5.555555555555555), TrajEntry(train_perf=1.0, incumbent_id=2, incumbent=Configuration:
, ta_runs=1, ta_time_used=1.8480315208435059, wallclock_time=3.0527870655059814, budget=5.555555555555555), TrajEntry(train_perf=1.0, incumbent_id=2, incumbent=Configuration:
data_loader:batch_size, Value: 165
encoder:__choice__, Value: 'NoEncoder'
feature_preprocessor:TruncatedSVD:target_dim, Value: 5
Expand All @@ -245,7 +245,7 @@ Print the final ensemble performance
optimizer:__choice__, Value: 'SGDOptimizer'
scaler:__choice__, Value: 'StandardScaler'
trainer:__choice__, Value: 'StandardTrainer'
, ta_runs=5, ta_time_used=13.114794254302979, wallclock_time=19.58250379562378, budget=5.555555555555555), TrajEntry(train_perf=1.0799881026204055, incumbent_id=3, incumbent=Configuration:
, ta_runs=5, ta_time_used=13.620249032974243, wallclock_time=20.430951833724976, budget=5.555555555555555), TrajEntry(train_perf=1.0799881026204055, incumbent_id=3, incumbent=Configuration:
data_loader:batch_size, Value: 68
encoder:__choice__, Value: 'NoEncoder'
feature_preprocessor:KernelPCA:kernel, Value: 'cosine'
Expand Down Expand Up @@ -276,7 +276,7 @@ Print the final ensemble performance
scaler:__choice__, Value: 'MinMaxScaler'
trainer:MixUpTrainer:alpha, Value: 0.3907472004119521
trainer:__choice__, Value: 'MixUpTrainer'
, ta_runs=10, ta_time_used=34.464011669158936, wallclock_time=46.46885395050049, budget=16.666666666666664), TrajEntry(train_perf=0.28945761605598963, incumbent_id=4, incumbent=Configuration:
, ta_runs=10, ta_time_used=36.47500991821289, wallclock_time=48.76981735229492, budget=16.666666666666664), TrajEntry(train_perf=0.28945761605598963, incumbent_id=4, incumbent=Configuration:
data_loader:batch_size, Value: 64
encoder:__choice__, Value: 'OneHotEncoder'
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
Expand Down Expand Up @@ -307,7 +307,7 @@ Print the final ensemble performance
optimizer:__choice__, Value: 'AdamOptimizer'
scaler:__choice__, Value: 'StandardScaler'
trainer:__choice__, Value: 'StandardTrainer'
, ta_runs=12, ta_time_used=38.52357006072998, wallclock_time=52.670743465423584, budget=16.666666666666664)]
, ta_runs=12, ta_time_used=40.92501258850098, wallclock_time=55.36291861534119, budget=16.666666666666664)]
{'r2': 0.9223648131866926}
| | Preprocessing | Estimator | Weight |
|---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
Expand All @@ -321,7 +321,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 43.464 seconds)
**Total running time of the script:** ( 5 minutes 51.950 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:
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8 changes: 4 additions & 4 deletions development/_sources/examples/20_basics/sg_execution_times.rst.txt
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Expand Up @@ -5,12 +5,12 @@

Computation times
=================
**11:27.940** total execution time for **examples_20_basics** files:
**11:37.060** total execution time for **examples_20_basics** files:

+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:43.464 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:51.950 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:35.924 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:37.322 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:08.553 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:07.788 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+

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