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Ravin Kohli: [FIX] Remove redundant categorical imputation (#375)
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12 changes: 6 additions & 6 deletions development/_sources/examples/20_basics/example_image_classification.rst.txt
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Expand Up @@ -87,14 +87,14 @@ Image Classification
Configuration(values={
'image_augmenter:GaussianBlur:use_augmenter': False,
'image_augmenter:GaussianNoise:use_augmenter': False,
'image_augmenter:RandomAffine:rotate': 171,
'image_augmenter:RandomAffine:scale_offset': 0.16968859827986923,
'image_augmenter:RandomAffine:shear': 42,
'image_augmenter:RandomAffine:translate_percent_offset': 0.0006590163048096454,
'image_augmenter:RandomAffine:rotate': 341,
'image_augmenter:RandomAffine:scale_offset': 0.395298372732557,
'image_augmenter:RandomAffine:shear': 23,
'image_augmenter:RandomAffine:translate_percent_offset': 0.30691858738922473,
'image_augmenter:RandomAffine:use_augmenter': True,
'image_augmenter:RandomCutout:use_augmenter': False,
'image_augmenter:Resize:use_augmenter': True,
'image_augmenter:ZeroPadAndCrop:percent': 0.058124084899998096,
'image_augmenter:ZeroPadAndCrop:percent': 0.1233844931832313,
'normalizer:__choice__': 'NoNormalizer',
})

Expand Down Expand Up @@ -175,7 +175,7 @@ Image Classification
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 5.506 seconds)
**Total running time of the script:** ( 0 minutes 5.581 seconds)


.. _sphx_glr_download_examples_20_basics_example_image_classification.py:
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46 changes: 21 additions & 25 deletions development/_sources/examples/20_basics/example_tabular_classification.rst.txt
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Expand Up @@ -134,7 +134,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f5f46714070>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f6d20d85100>
Expand Down Expand Up @@ -166,33 +166,29 @@ Print the final ensemble performance
.. code-block:: none
{'accuracy': 0.8497109826589595}
| | Preprocessing | Estimator | Weight |
|---:|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
| 0 | SimpleImputer,Variance Threshold,NoEncoder,PowerTransformer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
| 1 | SimpleImputer,Variance Threshold,NoEncoder,MinMaxScaler,KitchenSink | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
| 2 | None | CBLearner | 0.12 |
| 3 | None | SVMLearner | 0.12 |
| 4 | None | RFLearner | 0.08 |
| 5 | SimpleImputer,Variance Threshold,NoEncoder,MinMaxScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
| 6 | None | KNNLearner | 0.06 |
| 7 | SimpleImputer,Variance Threshold,OneHotEncoder,QuantileTransformer,PolynomialFeatures | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 8 | SimpleImputer,Variance Threshold,OneHotEncoder,MinMaxScaler,PolynomialFeatures | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 9 | SimpleImputer,Variance Threshold,OneHotEncoder,NoScaler,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 10 | None | LGBMLearner | 0.04 |
| 11 | None | ETLearner | 0.04 |
| 12 | SimpleImputer,Variance Threshold,OneHotEncoder,NoScaler,PolynomialFeatures | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 13 | SimpleImputer,Variance Threshold,OneHotEncoder,QuantileTransformer,PolynomialFeatures | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 14 | SimpleImputer,Variance Threshold,NoEncoder,PowerTransformer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 15 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 16 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| | Preprocessing | Estimator | Weight |
|---:|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
| 0 | SimpleImputer,Variance Threshold,NoEncoder,MinMaxScaler,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.18 |
| 1 | SimpleImputer,Variance Threshold,NoEncoder,NoScaler,KitchenSink | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
| 2 | SimpleImputer,Variance Threshold,NoEncoder,NoScaler,KitchenSink | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
| 3 | None | CBLearner | 0.12 |
| 4 | None | SVMLearner | 0.1 |
| 5 | None | RFLearner | 0.06 |
| 6 | None | KNNLearner | 0.06 |
| 7 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
| 8 | SimpleImputer,Variance Threshold,NoEncoder,StandardScaler,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 9 | None | LGBMLearner | 0.04 |
| 10 | SimpleImputer,Variance Threshold,OneHotEncoder,RobustScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 11 | SimpleImputer,Variance Threshold,OneHotEncoder,QuantileTransformer,KitchenSink | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 12 | None | ETLearner | 0.02 |
autoPyTorch results:
Dataset name: Australian
Optimisation Metric: accuracy
Best validation score: 0.8713450292397661
Number of target algorithm runs: 26
Number of successful target algorithm runs: 24
Number of crashed target algorithm runs: 1
Number of target algorithms that exceeded the time limit: 1
Number of target algorithm runs: 24
Number of successful target algorithm runs: 22
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 2
Number of target algorithms that exceeded the memory limit: 0
Expand All @@ -202,7 +198,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 22.134 seconds)
**Total running time of the script:** ( 5 minutes 26.257 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:
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27 changes: 13 additions & 14 deletions development/_sources/examples/20_basics/example_tabular_regression.rst.txt
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Expand Up @@ -125,7 +125,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f5fe1484d90>
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f6dbb9bcd90>
Expand Down Expand Up @@ -159,22 +159,21 @@ Print the final ensemble performance

.. code-block:: none
{'r2': 0.9408102126984811}
| | Preprocessing | Estimator | Weight |
|---:|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
| 0 | None | CBLearner | 0.44 |
| 1 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.44 |
| 2 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
| 3 | SimpleImputer,Variance Threshold,NoEncoder,RobustScaler,NoFeaturePreprocessing | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 4 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
{'r2': 0.9407884171054208}
| | Preprocessing | Estimator | Weight |
|---:|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
| 0 | None | CBLearner | 0.44 |
| 1 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.42 |
| 2 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
| 3 | None | LGBMLearner | 0.04 |
autoPyTorch results:
Dataset name: 41e1340b-8997-11ec-881e-d58abe4e22c6
Dataset name: 9c684c0d-89ce-11ec-8818-a1cc4bcb5e23
Optimisation Metric: r2
Best validation score: 0.8670098636440993
Number of target algorithm runs: 24
Number of successful target algorithm runs: 23
Number of target algorithm runs: 29
Number of successful target algorithm runs: 29
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 1
Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
Expand All @@ -184,7 +183,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 30.224 seconds)
**Total running time of the script:** ( 5 minutes 30.478 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:
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8 changes: 4 additions & 4 deletions development/_sources/examples/20_basics/sg_execution_times.rst.txt
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Expand Up @@ -5,12 +5,12 @@

Computation times
=================
**10:57.864** total execution time for **examples_20_basics** files:
**11:02.317** total execution time for **examples_20_basics** files:

+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:30.224 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:30.478 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:22.134 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:26.257 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:05.506 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:05.581 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+

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