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Eddie Bergman: Fix: keyword arguments to submit (#384)
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17 changes: 9 additions & 8 deletions development/_sources/examples/20_basics/example_image_classification.rst.txt
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Expand Up @@ -86,16 +86,17 @@ Image Classification
________________________________________
Configuration(values={
'image_augmenter:GaussianBlur:use_augmenter': False,
'image_augmenter:GaussianNoise:use_augmenter': False,
'image_augmenter:RandomAffine:rotate': 242,
'image_augmenter:RandomAffine:scale_offset': 0.33257410970986145,
'image_augmenter:RandomAffine:shear': 9,
'image_augmenter:RandomAffine:translate_percent_offset': 0.08322219618477589,
'image_augmenter:GaussianNoise:sigma_offset': 1.746304541085565,
'image_augmenter:GaussianNoise:use_augmenter': True,
'image_augmenter:RandomAffine:rotate': 60,
'image_augmenter:RandomAffine:scale_offset': 0.22600480469728468,
'image_augmenter:RandomAffine:shear': 4,
'image_augmenter:RandomAffine:translate_percent_offset': 0.14146389752228222,
'image_augmenter:RandomAffine:use_augmenter': True,
'image_augmenter:RandomCutout:p': 0.5931559928447478,
'image_augmenter:RandomCutout:p': 0.2650210115648416,
'image_augmenter:RandomCutout:use_augmenter': True,
'image_augmenter:Resize:use_augmenter': True,
'image_augmenter:ZeroPadAndCrop:percent': 0.04133682475059958,
'image_augmenter:ZeroPadAndCrop:percent': 0.27645895609259974,
'normalizer:__choice__': 'NoNormalizer',
})

Expand Down Expand Up @@ -176,7 +177,7 @@ Image Classification
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 7.050 seconds)
**Total running time of the script:** ( 0 minutes 6.603 seconds)


.. _sphx_glr_download_examples_20_basics_example_image_classification.py:
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8 changes: 4 additions & 4 deletions development/_sources/examples/20_basics/example_tabular_classification.rst.txt
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Expand Up @@ -134,7 +134,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f9cc997aeb0>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f1b03847400>
Expand Down Expand Up @@ -180,10 +180,10 @@ Print the final ensemble performance
Dataset name: Australian
Optimisation Metric: accuracy
Best validation score: 0.8713450292397661
Number of target algorithm runs: 24
Number of target algorithm runs: 23
Number of successful target algorithm runs: 21
Number of crashed target algorithm runs: 2
Number of target algorithms that exceeded the time limit: 1
Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
Expand All @@ -193,7 +193,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 18.469 seconds)
**Total running time of the script:** ( 5 minutes 21.126 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:
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10 changes: 5 additions & 5 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 0x7f9d6473cd90>
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f1b9f590700>
Expand Down Expand Up @@ -167,13 +167,13 @@ Print the final ensemble performance
| 2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
| 3 | None | LGBMLearner | 0.04 |
autoPyTorch results:
Dataset name: 81eeee0e-89dc-11ec-87d7-1970926e9c95
Dataset name: 351b9212-90d3-11ec-87dd-87e39c51dfff
Optimisation Metric: r2
Best validation score: 0.8670098636440993
Number of target algorithm runs: 28
Number of target algorithm runs: 27
Number of successful target algorithm runs: 25
Number of crashed target algorithm runs: 1
Number of target algorithms that exceeded the time limit: 2
Number of target algorithms that exceeded the time limit: 1
Number of target algorithms that exceeded the memory limit: 0
Expand All @@ -183,7 +183,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 26.883 seconds)
**Total running time of the script:** ( 5 minutes 33.360 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
=================
**10:52.402** total execution time for **examples_20_basics** files:
**11:01.090** total execution time for **examples_20_basics** files:

+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:26.883 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:33.360 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:18.469 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:21.126 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:07.050 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:06.603 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
37 changes: 18 additions & 19 deletions ...opment/_sources/examples/40_advanced/example_custom_configuration_space.rst.txt
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Expand Up @@ -163,7 +163,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f9cc9af9df0>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f1af8e73670>
Expand Down Expand Up @@ -207,13 +207,13 @@ Print the final ensemble performance
| 7 | None | LGBMLearner | 0.02 |
| 8 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
autoPyTorch results:
Dataset name: fc40e0e4-89df-11ec-87d7-1970926e9c95
Dataset name: a7fd7095-90d6-11ec-87dd-87e39c51dfff
Optimisation Metric: accuracy
Best validation score: 0.8596491228070176
Number of target algorithm runs: 19
Number of target algorithm runs: 18
Number of successful target algorithm runs: 16
Number of crashed target algorithm runs: 2
Number of target algorithms that exceeded the time limit: 1
Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
Expand Down Expand Up @@ -272,7 +272,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f9cc61a0850>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f1b02f724c0>
Expand Down Expand Up @@ -305,25 +305,24 @@ Print the final ensemble performance
{'accuracy': 0.861271676300578}
| | Preprocessing | Estimator | Weight |
|---:|:---------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
| 0 | None | RFLearner | 0.16 |
| 1 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,PowerTransformer,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
| 2 | None | SVMLearner | 0.12 |
| 3 | None | KNNLearner | 0.12 |
| 4 | None | LGBMLearner | 0.1 |
| 5 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,Normalizer,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
| 6 | None | ETLearner | 0.06 |
| 0 | None | LGBMLearner | 0.18 |
| 1 | None | SVMLearner | 0.16 |
| 2 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,PowerTransformer,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
| 3 | None | RFLearner | 0.14 |
| 4 | None | KNNLearner | 0.14 |
| 5 | None | ETLearner | 0.08 |
| 6 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
| 7 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
| 8 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
| 9 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
| 10 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,Normalizer,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 8 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,Normalizer,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 9 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
autoPyTorch results:
Dataset name: 62058c39-89e0-11ec-87d7-1970926e9c95
Dataset name: 0e8e6f13-90d7-11ec-87dd-87e39c51dfff
Optimisation Metric: accuracy
Best validation score: 0.8596491228070176
Number of target algorithm runs: 20
Number of successful target algorithm runs: 15
Number of successful target algorithm runs: 14
Number of crashed target algorithm runs: 5
Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the time limit: 1
Number of target algorithms that exceeded the memory limit: 0
Expand All @@ -333,7 +332,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 50.041 seconds)
**Total running time of the script:** ( 5 minutes 40.207 seconds)


.. _sphx_glr_download_examples_40_advanced_example_custom_configuration_space.py:
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10 changes: 5 additions & 5 deletions development/_sources/examples/40_advanced/example_parallel_n_jobs.rst.txt
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Expand Up @@ -36,14 +36,14 @@ with AutoPyTorch

.. code-block:: none
{'accuracy': 0.861271676300578}
{'accuracy': 0.8497109826589595}
autoPyTorch results:
Dataset name: 17179d6c-89df-11ec-87d7-1970926e9c95
Dataset name: cc63c157-90d5-11ec-87dd-87e39c51dfff
Optimisation Metric: accuracy
Best validation score: 0.8713450292397661
Number of target algorithm runs: 50
Number of target algorithm runs: 48
Number of successful target algorithm runs: 39
Number of crashed target algorithm runs: 8
Number of crashed target algorithm runs: 6
Number of target algorithms that exceeded the time limit: 3
Number of target algorithms that exceeded the memory limit: 0
Expand Down Expand Up @@ -121,7 +121,7 @@ with AutoPyTorch
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 25.812 seconds)
**Total running time of the script:** ( 5 minutes 27.594 seconds)


.. _sphx_glr_download_examples_40_advanced_example_parallel_n_jobs.py:
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10 changes: 5 additions & 5 deletions development/_sources/examples/40_advanced/example_plot_over_time.rst.txt
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Expand Up @@ -89,9 +89,9 @@ Task Definition

.. code-block:: none
[0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1
1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 0 1 1 0 0 0 1 1 1
0 1 1 0 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 0 1 0 1 1 0]
[0 0 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 1 0 0 1 0
0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 0
1 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 0 0 1 1]
Expand Down Expand Up @@ -121,7 +121,7 @@ API Instantiation and Searching
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f9cc9c1b790>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f1b02ba4b50>
Expand Down Expand Up @@ -186,7 +186,7 @@ _, ax = plt.subplots() <=== You can feed it to post-process the figure.

.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 2 minutes 19.530 seconds)
**Total running time of the script:** ( 2 minutes 15.313 seconds)


.. _sphx_glr_download_examples_40_advanced_example_plot_over_time.py:
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22 changes: 11 additions & 11 deletions development/_sources/examples/40_advanced/example_resampling_strategy.rst.txt
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Expand Up @@ -139,7 +139,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f9cc8d88d90>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f1b0331b880>
Expand Down Expand Up @@ -185,7 +185,7 @@ Print the final ensemble performance
| 8 | None | SVMLearner | 0.02 |
| 9 | None | KNNLearner | 0.02 |
autoPyTorch results:
Dataset name: cd177931-89e0-11ec-87d7-1970926e9c95
Dataset name: 730204c1-90d7-11ec-87dd-87e39c51dfff
Optimisation Metric: accuracy
Best validation score: 0.8713450292397661
Number of target algorithm runs: 20
Expand Down Expand Up @@ -255,7 +255,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f9cc92433d0>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f1b02ca8a60>
Expand Down Expand Up @@ -297,12 +297,12 @@ Print the final ensemble performance
| 4 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 5 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
autoPyTorch results:
Dataset name: 357c7684-89e1-11ec-87d7-1970926e9c95
Dataset name: d9c091dc-90d7-11ec-87dd-87e39c51dfff
Optimisation Metric: accuracy
Best validation score: 0.8626733083495604
Number of target algorithm runs: 14
Number of target algorithm runs: 13
Number of successful target algorithm runs: 12
Number of crashed target algorithm runs: 2
Number of crashed target algorithm runs: 1
Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
Expand Down Expand Up @@ -369,7 +369,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f9cb49fa130>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f1af8a2f280>
Expand Down Expand Up @@ -411,11 +411,11 @@ Print the final ensemble performance
| 5 | None | ETLearner | 0.04 |
| 6 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,RobustScaler,Nystroem | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
autoPyTorch results:
Dataset name: 9bbc12f2-89e1-11ec-87d7-1970926e9c95
Dataset name: 43559384-90d8-11ec-87dd-87e39c51dfff
Optimisation Metric: accuracy
Best validation score: 0.8362573099415205
Number of target algorithm runs: 20
Number of successful target algorithm runs: 19
Number of target algorithm runs: 19
Number of successful target algorithm runs: 18
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 1
Number of target algorithms that exceeded the memory limit: 0
Expand All @@ -427,7 +427,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 8 minutes 41.811 seconds)
**Total running time of the script:** ( 8 minutes 35.967 seconds)


.. _sphx_glr_download_examples_40_advanced_example_resampling_strategy.py:
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