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Ravin Kohli: [FIX] pipeline options in fit_pipeline (#466)
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‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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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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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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Extracting ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw
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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': 0.40736851695519793,
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'image_augmenter:GaussianBlur:sigma_offset': 1.9154521101106374,
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'image_augmenter:GaussianBlur:use_augmenter': True,
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'image_augmenter:GaussianNoise:sigma_offset': 2.1494393981863014,
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'image_augmenter:GaussianNoise:use_augmenter': True,
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'image_augmenter:RandomAffine:use_augmenter': False,
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'image_augmenter:RandomCutout:p': 0.7558153204326064,
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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:rotate': 264,
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'image_augmenter:RandomAffine:scale_offset': 0.1997598286556691,
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'image_augmenter:RandomAffine:shear': 38,
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'image_augmenter:RandomAffine:translate_percent_offset': 0.21531300486072077,
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'image_augmenter:RandomAffine:use_augmenter': True,
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'image_augmenter:RandomCutout:p': 0.24443754548888208,
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'image_augmenter:RandomCutout:use_augmenter': True,
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'image_augmenter:Resize:use_augmenter': True,
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'image_augmenter:ZeroPadAndCrop:percent': 0.08168973511042621,
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'image_augmenter:ZeroPadAndCrop:percent': 0.37202634450268485,
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'normalizer:__choice__': 'ImageNormalizer',
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})
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.. rst-class:: sphx-glr-timing
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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 0x7f7f3976b6a0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f1b6277e6a0>
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Optimisation Metric: accuracy
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Number of target algorithm runs: 27
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Number of successful target algorithm runs: 25
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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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.. _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 0x7f7ea94eb340>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f1ad2685730>
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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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Dataset name: 63413bef-1a43-11ed-8830-056390bd6e17
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Optimisation Metric: r2
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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:** ( 0 minutes 58.963 seconds)
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.. _sphx_glr_download_examples_20_basics_example_time_series_forecasting.py:

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

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Computation times
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=================
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**12:12.513** total execution time for **examples_20_basics** files:
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+----------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:34.705 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:35.870 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:20.861 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:31.873 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_time_series_forecasting.py` (``example_time_series_forecasting.py``) | 00:57.742 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_time_series_forecasting.py` (``example_time_series_forecasting.py``) | 00:58.963 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:07.417 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:05.806 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------------------+-----------+--------+

‎development/_sources/examples/40_advanced/example_custom_configuration_space.rst.txt

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{'accuracy': 0.8728323699421965}
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|---:|:---------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | None | LGBMLearner | 0.32 |
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| 1 | None | SVMLearner | 0.28 |
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| 2 | None | RFLearner | 0.26 |
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| 3 | None | ETLearner | 0.1 |
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| 5 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 0 | None | LGBMLearner | 0.36 |
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| 1 | None | RFLearner | 0.26 |
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| 2 | None | ETLearner | 0.14 |
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| 3 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 4 | None | SVMLearner | 0.08 |
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| 5 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,Normalizer,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 6 | None | KNNLearner | 0.02 |
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| 7 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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Number of successful target algorithm runs: 15
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Number of crashed target algorithm runs: 5
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**Total running time of the script:** ( 5 minutes 41.695 seconds)
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.. _sphx_glr_download_examples_40_advanced_example_custom_configuration_space.py:

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