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Aron Bahram: refactor: use progress_bar more explicitly as a thread (#1622)
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‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎development/_sources/examples/20_basic/example_classification.rst.txt

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‎development/_sources/examples/20_basic/example_multilabel_classification.rst.txt

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.. code-block:: none
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Fitting to the training data: 0%| | 0/60 [00:00<?, ?it/s, The total time budget for this task is 0:01:00] Fitting to the training data: 2%|1 | 1/60 [00:01<00:59, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 3%|3 | 2/60 [00:02<00:58, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 5%|5 | 3/60 [00:03<00:57, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 7%|6 | 4/60 [00:04<00:56, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 8%|8 | 5/60 [00:05<00:55, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 10%|# | 6/60 [00:06<00:54, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 100%|##########| 60/60 [00:06<00:00, 9.98it/s, The total time budget for this task is 0:01:00]
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Fitting to the training data: 0%| | 0/60 [00:00<?, ?it/s, The total time budget for this task is 0:01:00] Fitting to the training data: 2%|1 | 1/60 [00:01<00:59, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 3%|3 | 2/60 [00:02<00:58, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 5%|5 | 3/60 [00:03<00:57, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 7%|6 | 4/60 [00:04<00:56, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 8%|8 | 5/60 [00:05<00:55, 1.00s/it, The total time budget for this task is 0:01:00] Fitting to the training data: 100%|##########| 60/60 [00:05<00:00, 11.98it/s, The total time budget for this task is 0:01:00]
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AutoSklearnClassifier(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
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initial_configurations_via_metalearning=0,
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rank ensemble_weight type cost duration
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model_id
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2 1 1.0 random_forest 0.447294 3.220752
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2 1 1.0 random_forest 0.447294 3.921251
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.. code-block:: none
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{ 2: { 'balancing': Balancing(random_state=1),
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'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f71b4934550>,
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'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7fb838baa520>,
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'cost': 0.4472941828699525,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f71b492a190>,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb831eaebb0>,
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'ensemble_weight': 1.0,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f71b4934d30>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb838baa790>,
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'model_id': 2,
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'rank': 1,
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'sklearn_classifier': RandomForestClassifier(max_features=15, n_estimators=512, n_jobs=1,
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 17.146 seconds)
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**Total running time of the script:** ( 0 minutes 23.022 seconds)
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.. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

‎development/_sources/examples/20_basic/example_multioutput_regression.rst.txt

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‎development/_sources/examples/20_basic/example_regression.rst.txt

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‎development/_sources/examples/20_basic/sg_execution_times.rst.txt

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Computation times
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=================
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**06:13.995** total execution time for **examples_20_basic** files:
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**06:25.696** total execution time for **examples_20_basic** files:
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| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:05.582 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:04.776 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:55.913 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 02:00.267 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:55.355 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:57.631 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:17.146 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:23.022 | 0.0 MB |
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