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Ravin Kohli: add change log for release (#450)
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23 changes: 11 additions & 12 deletions development/_sources/examples/20_basics/example_image_classification.rst.txt
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Expand Up @@ -35,22 +35,22 @@ Image Classification
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz
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Extracting ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz
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Extracting ../datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
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Extracting ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
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Extracting ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw

Pipeline CS:
Expand Down Expand Up @@ -85,23 +85,22 @@ Image Classification
Pipeline Random Config:
________________________________________
Configuration(values={
'image_augmenter:GaussianBlur:sigma_min': 1.800750044920493,
'image_augmenter:GaussianBlur:sigma_offset': 0.0008507475449754942,
'image_augmenter:GaussianBlur:use_augmenter': True,
'image_augmenter:GaussianBlur:use_augmenter': False,
'image_augmenter:GaussianNoise:use_augmenter': False,
'image_augmenter:RandomAffine:use_augmenter': False,
'image_augmenter:RandomCutout:use_augmenter': False,
'image_augmenter:RandomCutout:p': 0.34114189827681496,
'image_augmenter:RandomCutout:use_augmenter': True,
'image_augmenter:Resize:use_augmenter': False,
'image_augmenter:ZeroPadAndCrop:percent': 0.3938396231176561,
'normalizer:__choice__': 'ImageNormalizer',
'image_augmenter:ZeroPadAndCrop:percent': 0.17619897373538618,
'normalizer:__choice__': 'NoNormalizer',
})

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

1-) preprocessing:
EarlyPreprocessing
Expand Down Expand Up @@ -173,7 +172,7 @@ Image Classification
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 7.321 seconds)
**Total running time of the script:** ( 0 minutes 7.995 seconds)


.. _sphx_glr_download_examples_20_basics_example_image_classification.py:
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24 changes: 12 additions & 12 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 0x7f2407c75af0>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f83c94a1970>
Expand Down Expand Up @@ -165,20 +165,20 @@ Print the final ensemble performance

.. code-block:: none
{'accuracy': 0.8670520231213873}
| | Preprocessing | Estimator | Weight |
|---:|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
| 0 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,FastICA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.56 |
| 1 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,Normalizer,KernelPCA | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.38 |
| 2 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,PCA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 3 | None | CBLearner | 0.02 |
| 4 | None | SVMLearner | 0.02 |
{'accuracy': 0.8728323699421965}
| | Preprocessing | Estimator | Weight |
|---:|:-------------------------------------------------------------------------------------------|:-------------------------------------------------------------|---------:|
| 0 | None | CBLearner | 0.5 |
| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,FastICA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.22 |
| 2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,PowerTransformer,Nystroem | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
| 3 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,PCA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
| 4 | None | RFLearner | 0.02 |
autoPyTorch results:
Dataset name: Australian
Optimisation Metric: accuracy
Best validation score: 0.8713450292397661
Number of target algorithm runs: 27
Number of successful target algorithm runs: 26
Number of target algorithm runs: 23
Number of successful target algorithm runs: 22
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 @@ -190,7 +190,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 24.577 seconds)
**Total running time of the script:** ( 5 minutes 27.372 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:
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21 changes: 11 additions & 10 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 0x7f248d0d5d90>
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f8459f30d90>
Expand Down Expand Up @@ -159,19 +159,20 @@ Print the final ensemble performance

.. code-block:: none
{'r2': 0.9407884171054208}
{'r2': 0.9412847640085195}
| | Preprocessing | Estimator | Weight |
|---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
| 0 | None | CBLearner | 0.44 |
| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.42 |
| 0 | None | CBLearner | 0.46 |
| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.4 |
| 2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
| 3 | None | LGBMLearner | 0.04 |
| 3 | None | LGBMLearner | 0.02 |
| 4 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
autoPyTorch results:
Dataset name: 59922def-0351-11ed-8824-d5cce4119db9
Dataset name: ba73302f-0375-11ed-8828-9bcdcaaf1ae6
Optimisation Metric: r2
Best validation score: 0.8670098636440993
Number of target algorithm runs: 24
Number of successful target algorithm runs: 22
Best validation score: 0.8669094525651709
Number of target algorithm runs: 22
Number of successful target algorithm runs: 20
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 2
Number of target algorithms that exceeded the memory limit: 0
Expand All @@ -183,7 +184,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 36.793 seconds)
**Total running time of the script:** ( 6 minutes 2.422 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:
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2 changes: 1 addition & 1 deletion development/_sources/examples/20_basics/example_time_series_forecasting.rst.txt
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Expand Up @@ -150,7 +150,7 @@ Search for an ensemble of machine learning algorithms
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 1 minutes 3.199 seconds)
**Total running time of the script:** ( 1 minutes 6.152 seconds)


.. _sphx_glr_download_examples_20_basics_example_time_series_forecasting.py:
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10 changes: 5 additions & 5 deletions development/_sources/examples/20_basics/sg_execution_times.rst.txt
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Expand Up @@ -5,14 +5,14 @@

Computation times
=================
**12:11.890** total execution time for **examples_20_basics** files:
**12:43.941** total execution time for **examples_20_basics** files:

+----------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:36.793 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 06:02.422 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:24.577 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:27.372 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_time_series_forecasting.py` (``example_time_series_forecasting.py``) | 01:03.199 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_time_series_forecasting.py` (``example_time_series_forecasting.py``) | 01:06.152 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:07.321 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:07.995 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------------+-----------+--------+

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