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Ravin Kohli: add change log for release (#450)
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‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip‎

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‎development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip‎

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‎development/_images/sphx_glr_example_plot_over_time_001.png‎

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‎development/_images/sphx_glr_example_plot_over_time_thumb.png‎

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‎development/_images/sphx_glr_example_visualization_001.png‎

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‎development/_sources/examples/20_basics/example_image_classification.rst.txt‎

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Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
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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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Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
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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
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Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
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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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Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
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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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Pipeline CS:
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Pipeline Random Config:
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________________________________________
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Configuration(values={
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'image_augmenter:GaussianBlur:sigma_min': 1.800750044920493,
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'image_augmenter:GaussianBlur:sigma_offset': 0.0008507475449754942,
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'image_augmenter:GaussianBlur:use_augmenter': True,
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'image_augmenter:GaussianBlur:use_augmenter': False,
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'image_augmenter:GaussianNoise:use_augmenter': False,
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'image_augmenter:RandomAffine:use_augmenter': False,
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'image_augmenter:RandomCutout:use_augmenter': False,
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'image_augmenter:RandomCutout:p': 0.34114189827681496,
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'image_augmenter:RandomCutout:use_augmenter': True,
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'image_augmenter:Resize:use_augmenter': False,
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'image_augmenter:ZeroPadAndCrop:percent': 0.3938396231176561,
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'normalizer:__choice__': 'ImageNormalizer',
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'image_augmenter:ZeroPadAndCrop:percent': 0.17619897373538618,
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'normalizer:__choice__': 'NoNormalizer',
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})
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Fitting the pipeline...
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________________________________________
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ImageClassificationPipeline
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________________________________________
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ImageNormalizer
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NoNormalizer
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1-) preprocessing:
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EarlyPreprocessing
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 7.321 seconds)
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**Total running time of the script:** ( 0 minutes 7.995 seconds)
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.. _sphx_glr_download_examples_20_basics_example_image_classification.py:

‎development/_sources/examples/20_basics/example_tabular_classification.rst.txt‎

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.. code-block:: none
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f2407c75af0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f83c94a1970>
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.. code-block:: none
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{'accuracy': 0.8670520231213873}
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| | Preprocessing | Estimator | Weight |
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|---:|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,FastICA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.56 |
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| 1 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,Normalizer,KernelPCA | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.38 |
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| 2 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,PCA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 3 | None | CBLearner | 0.02 |
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| 4 | None | SVMLearner | 0.02 |
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{'accuracy': 0.8728323699421965}
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| | Preprocessing | Estimator | Weight |
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|---:|:-------------------------------------------------------------------------------------------|:-------------------------------------------------------------|---------:|
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| 0 | None | CBLearner | 0.5 |
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| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,FastICA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.22 |
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| 2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,PowerTransformer,Nystroem | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 3 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,PCA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 4 | None | RFLearner | 0.02 |
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autoPyTorch results:
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Dataset name: Australian
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Optimisation Metric: accuracy
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Best validation score: 0.8713450292397661
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Number of target algorithm runs: 27
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Number of successful target algorithm runs: 26
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Number of target algorithm runs: 23
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Number of successful target algorithm runs: 22
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Number of crashed target algorithm runs: 0
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Number of target algorithms that exceeded the time limit: 1
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Number of target algorithms that exceeded the memory limit: 0
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**Total running time of the script:** ( 5 minutes 24.577 seconds)
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**Total running time of the script:** ( 5 minutes 27.372 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

‎development/_sources/examples/20_basics/example_tabular_regression.rst.txt‎

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.. code-block:: none
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f248d0d5d90>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f8459f30d90>
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.. code-block:: none
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{'r2': 0.9407884171054208}
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{'r2': 0.9412847640085195}
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| | Preprocessing | Estimator | Weight |
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|---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | None | CBLearner | 0.44 |
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| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.42 |
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| 0 | None | CBLearner | 0.46 |
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| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.4 |
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| 2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 3 | None | LGBMLearner | 0.04 |
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| 3 | None | LGBMLearner | 0.02 |
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| 4 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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autoPyTorch results:
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Dataset name: 59922def-0351-11ed-8824-d5cce4119db9
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Dataset name: ba73302f-0375-11ed-8828-9bcdcaaf1ae6
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Optimisation Metric: r2
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Best validation score: 0.8670098636440993
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Number of target algorithm runs: 24
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Number of successful target algorithm runs: 22
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Best validation score: 0.8669094525651709
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Number of target algorithm runs: 22
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Number of successful target algorithm runs: 20
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Number of crashed target algorithm runs: 0
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Number of target algorithms that exceeded the time limit: 2
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Number of target algorithms that exceeded the memory limit: 0
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**Total running time of the script:** ( 6 minutes 2.422 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

‎development/_sources/examples/20_basics/example_time_series_forecasting.rst.txt‎

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**Total running time of the script:** ( 1 minutes 3.199 seconds)
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**Total running time of the script:** ( 1 minutes 6.152 seconds)
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.. _sphx_glr_download_examples_20_basics_example_time_series_forecasting.py:

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