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Ravin Kohli: [RELEASE] Release v0.2 (automl#448)
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‎master/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip‎

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‎master/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip‎

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‎master/_images/sphx_glr_example_plot_over_time_001.png‎

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‎master/_images/sphx_glr_example_plot_over_time_thumb.png‎

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‎master/_images/sphx_glr_example_visualization_001.png‎

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‎master/_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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Extracting ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw
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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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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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________________________________________
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Configuration(values={
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'image_augmenter:GaussianBlur:use_augmenter': False,
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'image_augmenter:GaussianNoise:sigma_offset': 2.16870839870273,
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'image_augmenter:GaussianNoise:use_augmenter': True,
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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:p': 0.4136881910603881,
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'image_augmenter:RandomCutout:use_augmenter': True,
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'image_augmenter:RandomCutout:use_augmenter': False,
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'image_augmenter:Resize:use_augmenter': False,
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'image_augmenter:ZeroPadAndCrop:percent': 0.426471061733336,
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'image_augmenter:ZeroPadAndCrop:percent': 0.4845537861730235,
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'normalizer:__choice__': 'NoNormalizer',
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})
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 5.416 seconds)
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.. _sphx_glr_download_examples_20_basics_example_image_classification.py:

‎master/_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 0x7f11a36ddcd0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7efe1fa97520>
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.. code-block:: none
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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 | 0.68 |
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| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,FastICA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.28 |
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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 | ETLearner | 0.02 |
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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: 24
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Number of target algorithm runs: 22
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Number of successful target algorithm runs: 21
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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 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 35.766 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

‎master/_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 0x7f123bc83070>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7efeaf590eb0>
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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: e1ae53e5-0685-11ed-882e-b1ae9262a945
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Dataset name: d21a1548-0700-11ed-884a-abcc1edca430
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Best validation score: 0.8670098636440993
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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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Best validation score: 0.8669094525651709
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Number of target algorithm runs: 21
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Number of successful target algorithm runs: 20
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Number of crashed target algorithm runs: 1
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Number of target algorithms that exceeded the time limit: 0
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**Total running time of the script:** ( 6 minutes 2.017 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

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

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**Total running time of the script:** ( 0 minutes 59.130 seconds)
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.. _sphx_glr_download_examples_20_basics_example_time_series_forecasting.py:

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