Skip to content

Commit

Permalink
Matthias Feurer: Update regression metadata (#1038)
Browse files Browse the repository at this point in the history
  • Loading branch information
Github Actions committed Dec 16, 2020
1 parent 6bd20b7 commit e34973a
Show file tree
Hide file tree
Showing 47 changed files with 3,389 additions and 2,021 deletions.
Binary file modified development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip
View file
Binary file not shown.
Binary file modified development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip
View file
Binary file not shown.
Binary file modified development/_images/sphx_glr_example_pandas_train_test_001.png
View file
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Binary file modified development/_images/sphx_glr_example_pandas_train_test_thumb.png
View file
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
124 changes: 114 additions & 10 deletions development/_sources/examples/20_basic/example_classification.rst.txt
View file

Large diffs are not rendered by default.

4 changes: 2 additions & 2 deletions development/_sources/examples/20_basic/example_multilabel_classification.rst.txt
View file
Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@ problem. Details on multilabel classification can be found
.. code-block:: none
type_of_target=multilabel-indicator
[WARNING] [2020-12-11 17:25:12,368:AutoML(1):reuters] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[WARNING] [2020-12-16 15:43:26,831:AutoML(1):reuters] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[(1.000000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'random_forest', 'data_preprocessing:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessing:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessing:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessing:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.5, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, 'data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.01},
dataset_properties={
'task': 3,
Expand Down Expand Up @@ -131,7 +131,7 @@ problem. Details on multilabel classification can be found
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 19.850 seconds)
**Total running time of the script:** ( 0 minutes 18.793 seconds)


.. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:
Expand Down
63 changes: 34 additions & 29 deletions development/_sources/examples/20_basic/example_regression.rst.txt
View file

Large diffs are not rendered by default.

8 changes: 4 additions & 4 deletions development/_sources/examples/20_basic/sg_execution_times.rst.txt
View file
Original file line number Diff line number Diff line change
Expand Up @@ -5,12 +5,12 @@

Computation times
=================
**04:19.749** total execution time for **examples_20_basic** files:
**04:16.506** total execution time for **examples_20_basic** files:

+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 02:04.422 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 01:59.910 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 01:55.477 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:57.803 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:19.850 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:18.793 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
2 changes: 1 addition & 1 deletion development/_sources/examples/40_advanced/example_feature_types.rst.txt
View file
Original file line number Diff line number Diff line change
Expand Up @@ -93,7 +93,7 @@ types will be automatically inferred, as demonstrated in
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 15.510 seconds)
**Total running time of the script:** ( 0 minutes 15.363 seconds)


.. _sphx_glr_download_examples_40_advanced_example_feature_types.py:
Expand Down
951 changes: 518 additions & 433 deletions development/_sources/examples/40_advanced/example_get_pipeline_components.rst.txt
View file

Large diffs are not rendered by default.

58 changes: 33 additions & 25 deletions development/_sources/examples/40_advanced/example_metrics.rst.txt
View file
Original file line number Diff line number Diff line change
Expand Up @@ -274,40 +274,48 @@ Auto-sklearn. We also print the content of the module below with
*r2
################################################################################
Use predefined accuracy metric
[WARNING] [2020-12-11 17:25:40,044:AutoML(1):d6d58dae5b02e07797da6d4d126ac9b6] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
Accuracy score 0.944056 using accuracy
[WARNING] [2020-12-16 15:43:54,311:AutoML(1):d6d58dae5b02e07797da6d4d126ac9b6] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[WARNING] [2020-12-16 15:43:54,722:autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner] Could not find runs for instance cifar_10
[WARNING] [2020-12-16 15:43:54,729:autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner] Could not find runs for instance gtsrb-hog03
[WARNING] [2020-12-16 15:43:54,732:autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner] Could not find runs for instance kuzushiji-49
[WARNING] [2020-12-16 15:43:54,742:autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner] Could not find runs for instance svhn
[WARNING] [2020-12-16 15:43:54,829:autosklearn.metalearning.metalearning.kNearestDatasets.kND] Found no best configuration for instance cifar_10
[WARNING] [2020-12-16 15:43:54,829:autosklearn.metalearning.metalearning.kNearestDatasets.kND] Found no best configuration for instance svhn
[WARNING] [2020-12-16 15:43:54,829:autosklearn.metalearning.metalearning.kNearestDatasets.kND] Found no best configuration for instance gtsrb-hog03
[WARNING] [2020-12-16 15:43:54,829:autosklearn.metalearning.metalearning.kNearestDatasets.kND] Found no best configuration for instance kuzushiji-49
Accuracy score 0.951049 using accuracy
################################################################################
Use self defined accuracy metric
[WARNING] [2020-12-11 17:26:33,098:AutoML(1):d6d58dae5b02e07797da6d4d126ac9b6] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[WARNING] [2020-12-11 17:26:33,099:AutoMLSMBO(1)::d6d58dae5b02e07797da6d4d126ac9b6] Could not find meta-data directory /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/files/accu_binary.classification_dense
[WARNING] [2020-12-16 15:44:50,132:AutoML(1):d6d58dae5b02e07797da6d4d126ac9b6] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[WARNING] [2020-12-16 15:44:50,133:AutoMLSMBO(1)::d6d58dae5b02e07797da6d4d126ac9b6] Could not find meta-data directory /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/files/accu_binary.classification_dense
Accuracy score 0.958042 using accu
################################################################################
Use self defined error metric
[WARNING] [2020-12-11 17:27:29,188:AutoML(1):d6d58dae5b02e07797da6d4d126ac9b6] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[WARNING] [2020-12-11 17:27:29,189:AutoMLSMBO(1)::d6d58dae5b02e07797da6d4d126ac9b6] Could not find meta-data directory /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/files/error_binary.classification_dense
[WARNING] [2020-12-16 15:45:45,814:AutoML(1):d6d58dae5b02e07797da6d4d126ac9b6] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[WARNING] [2020-12-16 15:45:45,815:AutoMLSMBO(1)::d6d58dae5b02e07797da6d4d126ac9b6] Could not find meta-data directory /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/files/error_binary.classification_dense
Error rate -0.041958 using error
################################################################################
Use self defined accuracy with additional argument
[WARNING] [2020-12-11 17:28:28,708:AutoML(1):d6d58dae5b02e07797da6d4d126ac9b6] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[WARNING] [2020-12-11 17:28:28,709:AutoMLSMBO(1)::d6d58dae5b02e07797da6d4d126ac9b6] Could not find meta-data directory /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/files/accu_add_binary.classification_dense
Accuracy score 0.958042 using accu_add
[WARNING] [2020-12-16 15:46:47,618:AutoML(1):d6d58dae5b02e07797da6d4d126ac9b6] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[WARNING] [2020-12-16 15:46:47,619:AutoMLSMBO(1)::d6d58dae5b02e07797da6d4d126ac9b6] Could not find meta-data directory /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/files/accu_add_binary.classification_dense
Accuracy score 0.944056 using accu_add
################################################################################
Use self defined error with additional argument
[WARNING] [2020-12-11 17:29:23,901:AutoML(1):d6d58dae5b02e07797da6d4d126ac9b6] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[WARNING] [2020-12-11 17:29:23,901:AutoMLSMBO(1)::d6d58dae5b02e07797da6d4d126ac9b6] Could not find meta-data directory /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/files/error_add_binary.classification_dense
[WARNING] [2020-12-11 17:29:27,247:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:29:30,769:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:29:34,730:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:29:36,684:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:29:39,497:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:29:40,855:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:29:43,991:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:29:48,859:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:29:50,217:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:29:55,315:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:30:00,888:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:30:06,651:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-11 17:30:12,986:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:47:45,474:AutoML(1):d6d58dae5b02e07797da6d4d126ac9b6] Capping the per_run_time_limit to 29.0 to have time for a least 2 models in each process.
[WARNING] [2020-12-16 15:47:45,475:AutoMLSMBO(1)::d6d58dae5b02e07797da6d4d126ac9b6] Could not find meta-data directory /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/files/error_add_binary.classification_dense
[WARNING] [2020-12-16 15:47:48,900:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:47:52,752:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:47:56,752:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:47:58,794:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:48:01,699:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:48:03,057:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:48:06,223:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:48:11,151:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:48:12,536:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:48:17,856:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:48:23,564:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:48:29,355:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
[WARNING] [2020-12-16 15:48:35,684:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
Error rate 0.615385 using error_add
Expand All @@ -316,7 +324,7 @@ Auto-sklearn. We also print the content of the module below with
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 4 minutes 38.610 seconds)
**Total running time of the script:** ( 4 minutes 47.168 seconds)


.. _sphx_glr_download_examples_40_advanced_example_metrics.py:
Expand Down
12 changes: 10 additions & 2 deletions development/_sources/examples/40_advanced/example_pandas_train_test.rst.txt
View file
Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,15 @@ list or numpy arrays as there is no per-column dtype (further details in the exa
A13 float64
A14 float64
dtype: object
Accuracy score 0.881159420289855
[WARNING] [2020-12-16 15:48:41,976:autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner] Could not find runs for instance cifar_10
[WARNING] [2020-12-16 15:48:41,982:autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner] Could not find runs for instance gtsrb-hog03
[WARNING] [2020-12-16 15:48:41,985:autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner] Could not find runs for instance kuzushiji-49
[WARNING] [2020-12-16 15:48:41,995:autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner] Could not find runs for instance svhn
[WARNING] [2020-12-16 15:48:42,083:autosklearn.metalearning.metalearning.kNearestDatasets.kND] Found no best configuration for instance svhn
[WARNING] [2020-12-16 15:48:42,083:autosklearn.metalearning.metalearning.kNearestDatasets.kND] Found no best configuration for instance cifar_10
[WARNING] [2020-12-16 15:48:42,084:autosklearn.metalearning.metalearning.kNearestDatasets.kND] Found no best configuration for instance gtsrb-hog03
[WARNING] [2020-12-16 15:48:42,084:autosklearn.metalearning.metalearning.kNearestDatasets.kND] Found no best configuration for instance kuzushiji-49
Accuracy score 0.8753623188405797
Expand Down Expand Up @@ -206,7 +214,7 @@ list or numpy arrays as there is no per-column dtype (further details in the exa
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 1 minutes 51.911 seconds)
**Total running time of the script:** ( 1 minutes 56.272 seconds)


.. _sphx_glr_download_examples_40_advanced_example_pandas_train_test.py:
Expand Down
Loading

0 comments on commit e34973a

Please sign in to comment.