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Lukas Strack: Change treatment of generic column type object (#1415)
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364 changes: 188 additions & 176 deletions development/_sources/examples/20_basic/example_classification.rst.txt
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10 changes: 5 additions & 5 deletions development/_sources/examples/20_basic/example_multilabel_classification.rst.txt
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Expand Up @@ -155,7 +155,7 @@ View the models found by auto-sklearn
rank ensemble_weight type cost duration
model_id
2 1 1.0 random_forest 0.447294 4.123396
2 1 1.0 random_forest 0.447294 3.062512
Expand Down Expand Up @@ -183,11 +183,11 @@ Print the final ensemble constructed by auto-sklearn
.. code-block:: none
{ 2: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7fccc250c400>,
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f14c2a60850>,
'cost': 0.4472941828699525,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fccc64a19d0>,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f14c2a60700>,
'ensemble_weight': 1.0,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fccc250ca90>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f14c29e8d90>,
'model_id': 2,
'rank': 1,
'sklearn_classifier': RandomForestClassifier(max_features=15, n_estimators=512, n_jobs=1,
Expand Down Expand Up @@ -264,7 +264,7 @@ Get the Score of the final ensemble
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 25.450 seconds)
**Total running time of the script:** ( 0 minutes 28.536 seconds)


.. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:
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41 changes: 25 additions & 16 deletions development/_sources/examples/20_basic/example_multioutput_regression.rst.txt
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Expand Up @@ -123,9 +123,10 @@ View the models found by auto-sklearn

.. code-block:: none
rank ensemble_weight type cost duration
model_id
17 1 1.0 gaussian_process 0.000635 2.055573
rank ensemble_weight type cost duration
model_id
2 1 0.86 random_forest 0.146273 2.060702
14 2 0.14 random_forest 0.272872 1.570336
Expand All @@ -152,17 +153,25 @@ Print the final ensemble constructed by auto-sklearn

.. code-block:: none
{ 17: { 'cost': 0.0006351259927256381,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fccc4f581c0>,
'ensemble_weight': 1.0,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fccc5243100>,
'model_id': 17,
'rank': 1,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fccc9b3a4f0>,
'sklearn_regressor': GaussianProcessRegressor(alpha=0.01360809010989745,
kernel=RBF(length_scale=[1, 1, 1, 1, 1]),
n_restarts_optimizer=10, normalize_y=True,
random_state=1)}}
{ 2: { 'cost': 0.14627256489466145,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f14c2a546d0>,
'ensemble_weight': 0.86,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f14c167d640>,
'model_id': 2,
'rank': 1,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f14c167dee0>,
'sklearn_regressor': RandomForestRegressor(max_features=1.0, n_estimators=512, n_jobs=1,
random_state=1, warm_start=True)},
14: { 'cost': 0.2728716376042456,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f14c1a812b0>,
'ensemble_weight': 0.14,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f14bf25cfa0>,
'model_id': 14,
'rank': 2,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f14c3aa06d0>,
'sklearn_regressor': RandomForestRegressor(criterion='friedman_mse', max_features=0.8784959154058796,
min_samples_leaf=7, min_samples_split=3, n_estimators=512,
n_jobs=1, random_state=1, warm_start=True)}}
Expand Down Expand Up @@ -190,7 +199,7 @@ Get the Score of the final ensemble

.. code-block:: none
R2 score: 0.9994868491377081
R2 score: 0.8344553031938005
Expand Down Expand Up @@ -443,7 +452,7 @@ Get the configuration space
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 2 minutes 3.311 seconds)
**Total running time of the script:** ( 2 minutes 0.675 seconds)


.. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:
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74 changes: 43 additions & 31 deletions development/_sources/examples/20_basic/example_regression.rst.txt
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Expand Up @@ -128,9 +128,10 @@ View the models found by auto-sklearn
rank ensemble_weight type cost duration
model_id
25 1 0.30 sgd 0.436679 0.800863
33 2 0.36 liblinear_svr 0.443974 0.801987
6 3 0.34 ard_regression 0.455042 0.850376
31 1 0.48 ard_regression 0.428169 0.928177
25 2 0.26 sgd 0.436679 0.658780
33 3 0.20 liblinear_svr 0.472547 0.763788
29 4 0.06 ard_regression 0.493390 0.648472
Expand All @@ -157,39 +158,50 @@ Print the final ensemble constructed by auto-sklearn

.. code-block:: none
{ 6: { 'cost': 0.4550418898836528,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fccc66baf10>,
'ensemble_weight': 0.34,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fccc51d33d0>,
'model_id': 6,
'rank': 3,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fccc29c6e50>,
'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788, alpha_2=2.2118001735899097e-07,
copy_X=False, lambda_1=1.2037591637980971e-06,
lambda_2=4.358378124977852e-09,
threshold_lambda=1136.5286041327277, tol=0.021944240404849075)},
25: { 'cost': 0.43667876507897496,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fccc51ed460>,
'ensemble_weight': 0.3,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fccc9cb62b0>,
{ 25: { 'cost': 0.43667876507897496,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f14c1a47250>,
'ensemble_weight': 0.26,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f14c167de80>,
'model_id': 25,
'rank': 1,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fccc9cb6670>,
'rank': 2,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f14c167d640>,
'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654, epsilon=0.012150149892783745,
eta0=0.016444224834275295, l1_ratio=1.7462342366289323e-09,
loss='epsilon_insensitive', max_iter=16, penalty='elasticnet',
power_t=0.21521743568582094, random_state=1,
tol=0.002431731981071206, warm_start=True)},
33: { 'cost': 0.44397426747343005,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fccc51edf70>,
'ensemble_weight': 0.36,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fccc4c4ce50>,
29: { 'cost': 0.49338956176198634,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f14c1a6cf40>,
'ensemble_weight': 0.06,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f14c3bd0ac0>,
'model_id': 29,
'rank': 4,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f14c3bd00a0>,
'sklearn_regressor': ARDRegression(alpha_1=0.0003658795897322683, alpha_2=8.92755085837738e-05,
copy_X=False, lambda_1=2.7018753584355872e-06,
lambda_2=1.345477555564763e-07,
threshold_lambda=28013.994606205768, tol=0.00033715305179437166)},
31: { 'cost': 0.428169158594341,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f14c188b850>,
'ensemble_weight': 0.48,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f14c17e7f70>,
'model_id': 31,
'rank': 1,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f14c17e7d90>,
'sklearn_regressor': ARDRegression(alpha_1=0.0008154544266182765, alpha_2=5.59538372768849e-10,
copy_X=False, lambda_1=0.0006960494791085091,
lambda_2=0.00035440029631338203,
threshold_lambda=6133.542143966113, tol=0.004462344005356406)},
33: { 'cost': 0.4725467145542245,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f14c2abd160>,
'ensemble_weight': 0.2,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f14c15b6520>,
'model_id': 33,
'rank': 2,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fccc4c4c4f0>,
'sklearn_regressor': LinearSVR(C=2.4238675236172087, dual=False, epsilon=0.007260829311745733,
'rank': 3,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f14bf1f9dc0>,
'sklearn_regressor': LinearSVR(C=71.4085586379967, dual=False, epsilon=0.03951022702573597,
loss='squared_epsilon_insensitive', random_state=1,
tol=0.00523281845966111)}}
tol=0.006317097934562396)}}
Expand Down Expand Up @@ -223,8 +235,8 @@ predicting the data mean has an R2 score of 0.

.. code-block:: none
Train R2 score: 0.5730992444209256
Test R2 score: 0.40603183069422977
Train R2 score: 0.5922043282790412
Test R2 score: 0.4073119977311549
Expand Down Expand Up @@ -269,7 +281,7 @@ the true value).

.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 1 minutes 58.538 seconds)
**Total running time of the script:** ( 1 minutes 59.102 seconds)


.. _sphx_glr_download_examples_20_basic_example_regression.py:
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10 changes: 5 additions & 5 deletions development/_sources/examples/20_basic/sg_execution_times.rst.txt
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Expand Up @@ -5,14 +5,14 @@

Computation times
=================
**06:27.803** total execution time for **examples_20_basic** files:
**06:38.123** total execution time for **examples_20_basic** files:

+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 02:03.311 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:09.811 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:00.504 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 02:00.675 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:58.538 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:59.102 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:25.450 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:28.536 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
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