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Ravin Kohli: [FIX] Datamanager in memory (#382)
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‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎development/_images/sphx_glr_example_plot_over_time_001.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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________________________________________
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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': 1.746304541085565,
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'image_augmenter:GaussianNoise:sigma_offset': 2.925784534700399,
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'image_augmenter:GaussianNoise:use_augmenter': True,
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'image_augmenter:RandomAffine:rotate': 60,
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'image_augmenter:RandomAffine:scale_offset': 0.22600480469728468,
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'image_augmenter:RandomAffine:shear': 4,
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'image_augmenter:RandomAffine:translate_percent_offset': 0.14146389752228222,
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'image_augmenter:RandomAffine:rotate': 79,
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'image_augmenter:RandomAffine:scale_offset': 0.3303982552097816,
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'image_augmenter:RandomAffine:shear': 22,
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'image_augmenter:RandomAffine:translate_percent_offset': 0.068262359976932,
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'image_augmenter:RandomAffine:use_augmenter': True,
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'image_augmenter:RandomCutout:p': 0.2650210115648416,
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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': 0.27645895609259974,
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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': 0.019686612530780023,
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'normalizer:__choice__': 'NoNormalizer',
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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 0x7f1b03847400>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fc57dba9700>
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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: 23
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Number of target algorithm runs: 24
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Number of successful target algorithm runs: 21
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Number of crashed target algorithm runs: 2
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Number of target algorithms that exceeded the time limit: 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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.. rst-class:: sphx-glr-timing
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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 0x7f1b9f590700>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7fc61981a700>
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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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autoPyTorch results:
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Dataset name: 351b9212-90d3-11ec-87dd-87e39c51dfff
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Dataset name: e30391f0-94ca-11ec-8816-6597b230fd31
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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: 27
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Number of successful target algorithm runs: 25
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Number of target algorithm runs: 28
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Number of successful target algorithm runs: 27
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Number of crashed target algorithm runs: 1
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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 time limit: 0
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.. rst-class:: sphx-glr-timing
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.. _sphx_glr_download_examples_20_basics_example_tabular_regression.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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**11:01.090** total execution time for **examples_20_basics** files:
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**10:50.955** 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``) | 05:33.360 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:27.461 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:21.126 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:16.919 | 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``) | 00:06.603 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:06.576 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+

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

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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fc578430910>
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| 7 | None | LGBMLearner | 0.02 |
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| 8 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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autoPyTorch results:
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Dataset name: a7fd7095-90d6-11ec-87dd-87e39c51dfff
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Number of target algorithm runs: 18
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Number of target algorithm runs: 19
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Number of crashed target algorithm runs: 2
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Number of target algorithms that exceeded the time limit: 1
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{'accuracy': 0.861271676300578}
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| | Preprocessing | Estimator | Weight |
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|---:|:---------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | None | LGBMLearner | 0.18 |
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| 1 | None | SVMLearner | 0.16 |
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| 2 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,PowerTransformer,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 3 | None | RFLearner | 0.14 |
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| 4 | None | KNNLearner | 0.14 |
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| 5 | None | ETLearner | 0.08 |
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| 6 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 0 | None | RFLearner | 0.16 |
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| 1 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,PowerTransformer,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 2 | None | SVMLearner | 0.12 |
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| 3 | None | KNNLearner | 0.12 |
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| 4 | None | LGBMLearner | 0.1 |
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| 5 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,Normalizer,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 6 | None | ETLearner | 0.06 |
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| 8 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,Normalizer,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 9 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 8 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 9 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 10 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,Normalizer,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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.. _sphx_glr_download_examples_40_advanced_example_custom_configuration_space.py:

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

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Dataset name: 719a6d78-94cd-11ec-8816-6597b230fd31
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.. _sphx_glr_download_examples_40_advanced_example_parallel_n_jobs.py:

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

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.. _sphx_glr_download_examples_40_advanced_example_plot_over_time.py:

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

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autoPyTorch results:
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autoPyTorch results:
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autoPyTorch results:
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Dataset name: 43559384-90d8-11ec-87dd-87e39c51dfff
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Number of target algorithm runs: 19
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.. _sphx_glr_download_examples_40_advanced_example_resampling_strategy.py:

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