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Ravin Kohli: Reduce run time of the test (#205)
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‎development/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: fe944f1b1d3cb4e7269e41b4f8b98b0d
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config: 1e4ee4ad449380a01eb5bd355b029d69
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎development/_downloads/38ebc52de63d1626596d1647c695c721/example_tabular_regression.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,

‎development/_downloads/3b0b756ccfcac69e6a1673e56f2f543f/example_visualization.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,

‎development/_downloads/3f9c66ebcc4532fdade3cdaa4d769bde/example_custom_configuration_space.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,

‎development/_downloads/4cbefcc88d68bf84110d315dc5fdb8e1/example_resampling_strategy.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,

‎development/_downloads/6ee656697d20c490e1d49bdbfb69d108/example_tabular_classification.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,

‎development/_downloads/aabde7b67f18388826b238683edce405/example_image_classification.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,

‎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_visualization_001.png

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‎development/_images/sphx_glr_example_visualization_thumb.png

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

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Extracting ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw
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Processing...
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/opt/hostedtoolcache/Python/3.8.9/x64/lib/python3.8/site-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)
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/opt/hostedtoolcache/Python/3.8.10/x64/lib/python3.8/site-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)
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return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
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Done!
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Pipeline CS:
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Pipeline Random Config:
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________________________________________
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Configuration:
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image_augmenter:GaussianBlur:use_augmenter, Value: False
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image_augmenter:GaussianNoise:sigma_offset, Value: 0.9350664188119167
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:GaussianBlur:sigma_min, Value: 0.0234546653439216
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image_augmenter:GaussianBlur:sigma_offset, Value: 1.9419513237876684
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image_augmenter:GaussianBlur:use_augmenter, Value: True
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image_augmenter:GaussianNoise:use_augmenter, Value: False
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image_augmenter:RandomAffine:use_augmenter, Value: False
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image_augmenter:RandomCutout:use_augmenter, Value: False
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image_augmenter:Resize:use_augmenter, Value: True
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.2883176736666994
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normalizer:__choice__, Value: 'ImageNormalizer'
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.13952632480424082
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normalizer:__choice__, Value: 'NoNormalizer'
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Fitting the pipeline...
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________________________________________
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ImageClassificationPipeline
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________________________________________
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0-) normalizer:
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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 7.534 seconds)
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**Total running time of the script:** ( 0 minutes 9.639 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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<smac.runhistory.runhistory.RunHistory object at 0x7f3d47b451f0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7fdfb2391cd0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0019197463989257812, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.003583669662475586, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=1, ta_time_used=5.061821699142456, wallclock_time=6.486589670181274, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
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, ta_runs=1, ta_time_used=5.496156692504883, wallclock_time=7.0571489334106445, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 224
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:Nystroem:kernel, Value: 'cosine'
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scaler:__choice__, Value: 'NoScaler'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=8, ta_time_used=74.99343538284302, wallclock_time=92.06234908103943, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=3, incumbent=Configuration:
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, ta_runs=8, ta_time_used=76.34314274787903, wallclock_time=94.93465065956116, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=3, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=11, ta_time_used=99.09096908569336, wallclock_time=123.97066354751587, budget=16.666666666666664)]
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, ta_runs=11, ta_time_used=101.17933344841003, wallclock_time=128.4708001613617, budget=16.666666666666664)]
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{'accuracy': 0.8670520231213873}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 6 minutes 2.932 seconds)
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**Total running time of the script:** ( 6 minutes 6.848 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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<smac.runhistory.runhistory.RunHistory object at 0x7f3d2f654f40> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7fdfa8dea670> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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encoder:__choice__, Value: 'OneHotEncoder'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018334388732910156, budget=0), TrajEntry(train_perf=0.37478272821074954, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0019443035125732422, budget=0), TrajEntry(train_perf=0.8271799717423096, incumbent_id=1, incumbent=Configuration:
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, ta_runs=1, ta_time_used=4.07157826423645, wallclock_time=7.617934226989746, budget=5.555555555555555)]
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{'r2': 0.9199487836494068}
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, ta_runs=1, ta_time_used=4.774270296096802, wallclock_time=8.881023645401001, budget=5.555555555555555), TrajEntry(train_perf=0.5705056242970682, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 220
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:PowerTransformer:standardize, Value: True
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feature_preprocessor:__choice__, Value: 'PowerTransformer'
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imputer:categorical_strategy, Value: 'constant_!missing!'
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imputer:numerical_strategy, Value: 'median'
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lr_scheduler:ExponentialLR:gamma, Value: 0.7297909296891054
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lr_scheduler:__choice__, Value: 'ExponentialLR'
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network_backbone:MLPBackbone:activation, Value: 'sigmoid'
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network_backbone:MLPBackbone:num_groups, Value: 1
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network_backbone:MLPBackbone:num_units_1, Value: 53
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network_backbone:MLPBackbone:use_dropout, Value: False
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network_backbone:__choice__, Value: 'MLPBackbone'
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network_embedding:LearnedEntityEmbedding:dimension_reduction_0, Value: 0.49162398882471625
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network_embedding:LearnedEntityEmbedding:dimension_reduction_1, Value: 0.9738223543779865
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network_embedding:LearnedEntityEmbedding:min_unique_values_for_embedding, Value: 3
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network_embedding:__choice__, Value: 'LearnedEntityEmbedding'
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network_head:__choice__, Value: 'fully_connected'
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network_head:fully_connected:activation, Value: 'tanh'
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network_head:fully_connected:num_layers, Value: 2
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network_head:fully_connected:units_layer_1, Value: 84
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network_init:XavierInit:bias_strategy, Value: 'Normal'
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network_init:__choice__, Value: 'XavierInit'
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optimizer:SGDOptimizer:lr, Value: 0.07384606967030707
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optimizer:SGDOptimizer:momentum, Value: 0.41566496944512654
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optimizer:SGDOptimizer:weight_decay, Value: 0.002766441795903385
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optimizer:__choice__, Value: 'SGDOptimizer'
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scaler:__choice__, Value: 'MinMaxScaler'
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=2, ta_time_used=9.929801225662231, wallclock_time=15.717073202133179, budget=5.555555555555555), TrajEntry(train_perf=0.21935886392409365, incumbent_id=3, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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imputer:categorical_strategy, Value: 'most_frequent'
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imputer:numerical_strategy, Value: 'mean'
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lr_scheduler:ReduceLROnPlateau:factor, Value: 0.1
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lr_scheduler:ReduceLROnPlateau:mode, Value: 'min'
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lr_scheduler:ReduceLROnPlateau:patience, Value: 10
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lr_scheduler:__choice__, Value: 'ReduceLROnPlateau'
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network_backbone:ShapedMLPBackbone:activation, Value: 'relu'
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network_backbone:ShapedMLPBackbone:max_units, Value: 200
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network_backbone:ShapedMLPBackbone:mlp_shape, Value: 'funnel'
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network_backbone:ShapedMLPBackbone:num_groups, Value: 5
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network_backbone:ShapedMLPBackbone:output_dim, Value: 200
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network_backbone:ShapedMLPBackbone:use_dropout, Value: False
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network_backbone:__choice__, Value: 'ShapedMLPBackbone'
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network_embedding:__choice__, Value: 'NoEmbedding'
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network_head:__choice__, Value: 'fully_connected'
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network_head:fully_connected:activation, Value: 'relu'
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network_head:fully_connected:num_layers, Value: 2
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network_head:fully_connected:units_layer_1, Value: 128
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network_init:XavierInit:bias_strategy, Value: 'Normal'
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network_init:__choice__, Value: 'XavierInit'
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optimizer:AdamOptimizer:beta1, Value: 0.9
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optimizer:AdamOptimizer:beta2, Value: 0.9
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optimizer:AdamOptimizer:lr, Value: 0.01
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optimizer:AdamOptimizer:weight_decay, Value: 0.0
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optimizer:__choice__, Value: 'AdamOptimizer'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=11, ta_time_used=64.2962703704834, wallclock_time=93.74913358688354, budget=16.666666666666664)]
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{'r2': 0.9189154709278342}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.58 |
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| 1 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.34 |
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| 2 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 0 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.64 |
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| 1 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.36 |
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 14.190 seconds)
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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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**11:38.195** total execution time for **examples_20_basics** files:
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**11:30.678** total execution time for **examples_20_basics** files:
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 06:02.932 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 06:06.848 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:27.729 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:14.190 | 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``) | 00:07.534 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:09.639 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+

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