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fraimondo committed Sep 26, 2024
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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: dbfb0490f2ca2c74fd7ce76fa14cb513
config: ecdce057ffc86afd58c7ba1f3d2863a8
tags: 645f666f9bcd5a90fca523b33c5a78b7
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90 changes: 45 additions & 45 deletions ...review/pr-273/_sources/auto_examples/00_starting/plot_cm_acc_multiclass.rst.txt
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Expand Up @@ -70,13 +70,13 @@ Set the logging level to info to see extra information

.. code-block:: none
2024-09-26 13:12:05,373 - julearn - INFO - ===== Lib Versions =====
2024-09-26 13:12:05,374 - julearn - INFO - numpy: 1.26.4
2024-09-26 13:12:05,374 - julearn - INFO - scipy: 1.14.1
2024-09-26 13:12:05,374 - julearn - INFO - sklearn: 1.4.2
2024-09-26 13:12:05,374 - julearn - INFO - pandas: 2.1.4
2024-09-26 13:12:05,374 - julearn - INFO - julearn: 0.3.4.dev23
2024-09-26 13:12:05,374 - julearn - INFO - ========================
2024-09-26 13:20:49,090 - julearn - INFO - ===== Lib Versions =====
2024-09-26 13:20:49,090 - julearn - INFO - numpy: 1.26.4
2024-09-26 13:20:49,090 - julearn - INFO - scipy: 1.14.1
2024-09-26 13:20:49,090 - julearn - INFO - sklearn: 1.4.2
2024-09-26 13:20:49,090 - julearn - INFO - pandas: 2.1.4
2024-09-26 13:20:49,090 - julearn - INFO - julearn: 0.3.4.dev25
2024-09-26 13:20:49,090 - julearn - INFO - ========================
Expand Down Expand Up @@ -151,39 +151,39 @@ vector machine classifier.

.. code-block:: none
2024-09-26 13:12:05,378 - julearn - INFO - ==== Input Data ====
2024-09-26 13:12:05,378 - julearn - INFO - Using dataframe as input
2024-09-26 13:12:05,378 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length']
2024-09-26 13:12:05,378 - julearn - INFO - Target: species
2024-09-26 13:12:05,378 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length']
2024-09-26 13:12:05,378 - julearn - INFO - X_types:{}
2024-09-26 13:12:05,379 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous.
2024-09-26 13:20:49,094 - julearn - INFO - ==== Input Data ====
2024-09-26 13:20:49,094 - julearn - INFO - Using dataframe as input
2024-09-26 13:20:49,094 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length']
2024-09-26 13:20:49,094 - julearn - INFO - Target: species
2024-09-26 13:20:49,094 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length']
2024-09-26 13:20:49,094 - julearn - INFO - X_types:{}
2024-09-26 13:20:49,094 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous.
/home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous.
warn_with_log(
2024-09-26 13:12:05,380 - julearn - INFO - ====================
2024-09-26 13:12:05,380 - julearn - INFO -
2024-09-26 13:12:05,380 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-09-26 13:12:05,380 - julearn - INFO - Step added
2024-09-26 13:12:05,380 - julearn - INFO - Adding step svm that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-09-26 13:12:05,380 - julearn - INFO - Step added
2024-09-26 13:12:05,381 - julearn - INFO - = Model Parameters =
2024-09-26 13:12:05,381 - julearn - INFO - ====================
2024-09-26 13:12:05,381 - julearn - INFO -
2024-09-26 13:12:05,381 - julearn - INFO - = Data Information =
2024-09-26 13:12:05,381 - julearn - INFO - Problem type: classification
2024-09-26 13:12:05,381 - julearn - INFO - Number of samples: 120
2024-09-26 13:12:05,381 - julearn - INFO - Number of features: 3
2024-09-26 13:12:05,381 - julearn - INFO - ====================
2024-09-26 13:12:05,381 - julearn - INFO -
2024-09-26 13:12:05,381 - julearn - INFO - Number of classes: 3
2024-09-26 13:12:05,381 - julearn - INFO - Target type: object
2024-09-26 13:12:05,382 - julearn - INFO - Class distributions: species
2024-09-26 13:20:49,095 - julearn - INFO - ====================
2024-09-26 13:20:49,095 - julearn - INFO -
2024-09-26 13:20:49,095 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-09-26 13:20:49,095 - julearn - INFO - Step added
2024-09-26 13:20:49,095 - julearn - INFO - Adding step svm that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-09-26 13:20:49,095 - julearn - INFO - Step added
2024-09-26 13:20:49,096 - julearn - INFO - = Model Parameters =
2024-09-26 13:20:49,096 - julearn - INFO - ====================
2024-09-26 13:20:49,096 - julearn - INFO -
2024-09-26 13:20:49,096 - julearn - INFO - = Data Information =
2024-09-26 13:20:49,096 - julearn - INFO - Problem type: classification
2024-09-26 13:20:49,096 - julearn - INFO - Number of samples: 120
2024-09-26 13:20:49,096 - julearn - INFO - Number of features: 3
2024-09-26 13:20:49,096 - julearn - INFO - ====================
2024-09-26 13:20:49,096 - julearn - INFO -
2024-09-26 13:20:49,096 - julearn - INFO - Number of classes: 3
2024-09-26 13:20:49,096 - julearn - INFO - Target type: object
2024-09-26 13:20:49,097 - julearn - INFO - Class distributions: species
versicolor 40
virginica 40
setosa 40
Name: count, dtype: int64
2024-09-26 13:12:05,382 - julearn - INFO - Using outer CV scheme RepeatedKFold(n_repeats=5, n_splits=5, random_state=200) (incl. final model)
2024-09-26 13:12:05,382 - julearn - INFO - Multi-class classification problem detected #classes = 3.
2024-09-26 13:20:49,097 - julearn - INFO - Using outer CV scheme RepeatedKFold(n_repeats=5, n_splits=5, random_state=200) (incl. final model)
2024-09-26 13:20:49,097 - julearn - INFO - Multi-class classification problem detected #classes = 3.
Expand Down Expand Up @@ -238,8 +238,8 @@ The scores dataframe has all the values for each CV split.
<tbody>
<tr>
<th>0</th>
<td>0.005651</td>
<td>0.003438</td>
<td>0.005100</td>
<td>0.002721</td>
<td>0.916667</td>
<td>96</td>
<td>24</td>
Expand All @@ -249,8 +249,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>1</th>
<td>0.004939</td>
<td>0.002758</td>
<td>0.004765</td>
<td>0.002666</td>
<td>0.833333</td>
<td>96</td>
<td>24</td>
Expand All @@ -260,8 +260,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>2</th>
<td>0.004837</td>
<td>0.002980</td>
<td>0.004633</td>
<td>0.002607</td>
<td>0.958333</td>
<td>96</td>
<td>24</td>
Expand All @@ -271,8 +271,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>3</th>
<td>0.005368</td>
<td>0.002776</td>
<td>0.004572</td>
<td>0.002621</td>
<td>0.916667</td>
<td>96</td>
<td>24</td>
Expand All @@ -282,8 +282,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>4</th>
<td>0.004813</td>
<td>0.003095</td>
<td>0.004550</td>
<td>0.002614</td>
<td>0.833333</td>
<td>96</td>
<td>24</td>
Expand Down Expand Up @@ -536,7 +536,7 @@ the heatmap with annotations.
.. rst-class:: sphx-glr-timing

**Total running time of the script:** (0 minutes 0.581 seconds)
**Total running time of the script:** (0 minutes 0.543 seconds)


.. _sphx_glr_download_auto_examples_00_starting_plot_cm_acc_multiclass.py:
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84 changes: 42 additions & 42 deletions ...eview/pr-273/_sources/auto_examples/00_starting/plot_example_regression.rst.txt
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Expand Up @@ -68,13 +68,13 @@ Set the logging level to info to see extra information.

.. code-block:: none
2024-09-26 13:12:07,190 - julearn - INFO - ===== Lib Versions =====
2024-09-26 13:12:07,190 - julearn - INFO - numpy: 1.26.4
2024-09-26 13:12:07,190 - julearn - INFO - scipy: 1.14.1
2024-09-26 13:12:07,190 - julearn - INFO - sklearn: 1.4.2
2024-09-26 13:12:07,190 - julearn - INFO - pandas: 2.1.4
2024-09-26 13:12:07,190 - julearn - INFO - julearn: 0.3.4.dev23
2024-09-26 13:12:07,190 - julearn - INFO - ========================
2024-09-26 13:20:50,858 - julearn - INFO - ===== Lib Versions =====
2024-09-26 13:20:50,858 - julearn - INFO - numpy: 1.26.4
2024-09-26 13:20:50,858 - julearn - INFO - scipy: 1.14.1
2024-09-26 13:20:50,858 - julearn - INFO - sklearn: 1.4.2
2024-09-26 13:20:50,858 - julearn - INFO - pandas: 2.1.4
2024-09-26 13:20:50,858 - julearn - INFO - julearn: 0.3.4.dev25
2024-09-26 13:20:50,858 - julearn - INFO - ========================
Expand Down Expand Up @@ -246,32 +246,32 @@ for scoring.

.. code-block:: none
2024-09-26 13:12:07,413 - julearn - INFO - ==== Input Data ====
2024-09-26 13:12:07,413 - julearn - INFO - Using dataframe as input
2024-09-26 13:12:07,413 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
2024-09-26 13:12:07,413 - julearn - INFO - Target: target
2024-09-26 13:12:07,413 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
2024-09-26 13:12:07,413 - julearn - INFO - X_types:{}
2024-09-26 13:12:07,413 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
2024-09-26 13:20:51,062 - julearn - INFO - ==== Input Data ====
2024-09-26 13:20:51,062 - julearn - INFO - Using dataframe as input
2024-09-26 13:20:51,062 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
2024-09-26 13:20:51,063 - julearn - INFO - Target: target
2024-09-26 13:20:51,063 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
2024-09-26 13:20:51,063 - julearn - INFO - X_types:{}
2024-09-26 13:20:51,063 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
/home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
warn_with_log(
2024-09-26 13:12:07,414 - julearn - INFO - ====================
2024-09-26 13:12:07,414 - julearn - INFO -
2024-09-26 13:12:07,414 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-09-26 13:12:07,415 - julearn - INFO - Step added
2024-09-26 13:12:07,415 - julearn - INFO - Adding step ridge that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-09-26 13:12:07,415 - julearn - INFO - Step added
2024-09-26 13:12:07,415 - julearn - INFO - = Model Parameters =
2024-09-26 13:12:07,415 - julearn - INFO - ====================
2024-09-26 13:12:07,415 - julearn - INFO -
2024-09-26 13:12:07,415 - julearn - INFO - = Data Information =
2024-09-26 13:12:07,415 - julearn - INFO - Problem type: regression
2024-09-26 13:12:07,416 - julearn - INFO - Number of samples: 309
2024-09-26 13:12:07,416 - julearn - INFO - Number of features: 10
2024-09-26 13:12:07,416 - julearn - INFO - ====================
2024-09-26 13:12:07,416 - julearn - INFO -
2024-09-26 13:12:07,416 - julearn - INFO - Target type: float64
2024-09-26 13:12:07,416 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model)
2024-09-26 13:20:51,064 - julearn - INFO - ====================
2024-09-26 13:20:51,064 - julearn - INFO -
2024-09-26 13:20:51,064 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-09-26 13:20:51,064 - julearn - INFO - Step added
2024-09-26 13:20:51,064 - julearn - INFO - Adding step ridge that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-09-26 13:20:51,064 - julearn - INFO - Step added
2024-09-26 13:20:51,065 - julearn - INFO - = Model Parameters =
2024-09-26 13:20:51,065 - julearn - INFO - ====================
2024-09-26 13:20:51,065 - julearn - INFO -
2024-09-26 13:20:51,065 - julearn - INFO - = Data Information =
2024-09-26 13:20:51,065 - julearn - INFO - Problem type: regression
2024-09-26 13:20:51,065 - julearn - INFO - Number of samples: 309
2024-09-26 13:20:51,065 - julearn - INFO - Number of features: 10
2024-09-26 13:20:51,065 - julearn - INFO - ====================
2024-09-26 13:20:51,065 - julearn - INFO -
2024-09-26 13:20:51,065 - julearn - INFO - Target type: float64
2024-09-26 13:20:51,065 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model)
Expand Down Expand Up @@ -326,8 +326,8 @@ The scores dataframe has all the values for each CV split.
<tbody>
<tr>
<th>0</th>
<td>0.005283</td>
<td>0.002497</td>
<td>0.005180</td>
<td>0.002367</td>
<td>-48.783874</td>
<td>247</td>
<td>62</td>
Expand All @@ -337,8 +337,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>1</th>
<td>0.004617</td>
<td>0.002762</td>
<td>0.004532</td>
<td>0.002317</td>
<td>-47.573568</td>
<td>247</td>
<td>62</td>
Expand All @@ -348,8 +348,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>2</th>
<td>0.004568</td>
<td>0.002346</td>
<td>0.004417</td>
<td>0.002350</td>
<td>-37.617474</td>
<td>247</td>
<td>62</td>
Expand All @@ -359,8 +359,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>3</th>
<td>0.004556</td>
<td>0.002379</td>
<td>0.004450</td>
<td>0.002325</td>
<td>-47.686852</td>
<td>247</td>
<td>62</td>
Expand All @@ -370,8 +370,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>4</th>
<td>0.004535</td>
<td>0.002339</td>
<td>0.004441</td>
<td>0.002309</td>
<td>-45.558655</td>
<td>248</td>
<td>61</td>
Expand Down Expand Up @@ -602,7 +602,7 @@ of true values vs predicted values.
.. rst-class:: sphx-glr-timing

**Total running time of the script:** (0 minutes 0.712 seconds)
**Total running time of the script:** (0 minutes 0.684 seconds)


.. _sphx_glr_download_auto_examples_00_starting_plot_example_regression.py:
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