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fraimondo committed Oct 17, 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: 7c1805c6b19cbbd67888993d7cce7cfd
config: c55beee4e03d16cd2fd1b55a8e31e999
tags: 645f666f9bcd5a90fca523b33c5a78b7
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90 changes: 45 additions & 45 deletions ...review/pr-275/_sources/auto_examples/00_starting/plot_cm_acc_multiclass.rst.txt
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Expand Up @@ -72,13 +72,13 @@ Set the logging level to info to see extra information
/home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead.
vstring = str(getattr(module, "__version__", None))
2024-10-17 09:50:57,919 - julearn - INFO - ===== Lib Versions =====
2024-10-17 09:50:57,919 - julearn - INFO - numpy: 1.26.4
2024-10-17 09:50:57,920 - julearn - INFO - scipy: 1.14.1
2024-10-17 09:50:57,920 - julearn - INFO - sklearn: 1.5.2
2024-10-17 09:50:57,920 - julearn - INFO - pandas: 2.2.3
2024-10-17 09:50:57,920 - julearn - INFO - julearn: 0.3.4.dev37
2024-10-17 09:50:57,920 - julearn - INFO - ========================
2024-10-17 13:45:36,555 - julearn - INFO - ===== Lib Versions =====
2024-10-17 13:45:36,555 - julearn - INFO - numpy: 1.26.4
2024-10-17 13:45:36,555 - julearn - INFO - scipy: 1.14.1
2024-10-17 13:45:36,555 - julearn - INFO - sklearn: 1.5.2
2024-10-17 13:45:36,555 - julearn - INFO - pandas: 2.2.3
2024-10-17 13:45:36,555 - julearn - INFO - julearn: 0.3.4.dev41
2024-10-17 13:45:36,555 - julearn - INFO - ========================
Expand Down Expand Up @@ -153,39 +153,39 @@ vector machine classifier.

.. code-block:: none
2024-10-17 09:50:57,923 - julearn - INFO - ==== Input Data ====
2024-10-17 09:50:57,923 - julearn - INFO - Using dataframe as input
2024-10-17 09:50:57,924 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length']
2024-10-17 09:50:57,924 - julearn - INFO - Target: species
2024-10-17 09:50:57,924 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length']
2024-10-17 09:50:57,924 - julearn - INFO - X_types:{}
2024-10-17 09:50:57,924 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous.
2024-10-17 13:45:36,559 - julearn - INFO - ==== Input Data ====
2024-10-17 13:45:36,559 - julearn - INFO - Using dataframe as input
2024-10-17 13:45:36,559 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length']
2024-10-17 13:45:36,559 - julearn - INFO - Target: species
2024-10-17 13:45:36,559 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length']
2024-10-17 13:45:36,559 - julearn - INFO - X_types:{}
2024-10-17 13:45:36,559 - 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(
2024-10-17 09:50:57,924 - julearn - INFO - ====================
2024-10-17 09:50:57,925 - julearn - INFO -
2024-10-17 09:50:57,925 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-10-17 09:50:57,925 - julearn - INFO - Step added
2024-10-17 09:50:57,925 - julearn - INFO - Adding step svm that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-10-17 09:50:57,925 - julearn - INFO - Step added
2024-10-17 09:50:57,925 - julearn - INFO - = Model Parameters =
2024-10-17 09:50:57,926 - julearn - INFO - ====================
2024-10-17 09:50:57,926 - julearn - INFO -
2024-10-17 09:50:57,926 - julearn - INFO - = Data Information =
2024-10-17 09:50:57,926 - julearn - INFO - Problem type: classification
2024-10-17 09:50:57,926 - julearn - INFO - Number of samples: 120
2024-10-17 09:50:57,926 - julearn - INFO - Number of features: 3
2024-10-17 09:50:57,926 - julearn - INFO - ====================
2024-10-17 09:50:57,926 - julearn - INFO -
2024-10-17 09:50:57,926 - julearn - INFO - Number of classes: 3
2024-10-17 09:50:57,926 - julearn - INFO - Target type: object
2024-10-17 09:50:57,927 - julearn - INFO - Class distributions: species
2024-10-17 13:45:36,560 - julearn - INFO - ====================
2024-10-17 13:45:36,560 - julearn - INFO -
2024-10-17 13:45:36,560 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-10-17 13:45:36,560 - julearn - INFO - Step added
2024-10-17 13:45:36,560 - julearn - INFO - Adding step svm that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-10-17 13:45:36,560 - julearn - INFO - Step added
2024-10-17 13:45:36,561 - julearn - INFO - = Model Parameters =
2024-10-17 13:45:36,561 - julearn - INFO - ====================
2024-10-17 13:45:36,561 - julearn - INFO -
2024-10-17 13:45:36,561 - julearn - INFO - = Data Information =
2024-10-17 13:45:36,561 - julearn - INFO - Problem type: classification
2024-10-17 13:45:36,561 - julearn - INFO - Number of samples: 120
2024-10-17 13:45:36,561 - julearn - INFO - Number of features: 3
2024-10-17 13:45:36,561 - julearn - INFO - ====================
2024-10-17 13:45:36,561 - julearn - INFO -
2024-10-17 13:45:36,561 - julearn - INFO - Number of classes: 3
2024-10-17 13:45:36,562 - julearn - INFO - Target type: object
2024-10-17 13:45:36,562 - julearn - INFO - Class distributions: species
versicolor 40
virginica 40
setosa 40
Name: count, dtype: int64
2024-10-17 09:50:57,927 - julearn - INFO - Using outer CV scheme RepeatedKFold(n_repeats=5, n_splits=5, random_state=200) (incl. final model)
2024-10-17 09:50:57,927 - julearn - INFO - Multi-class classification problem detected #classes = 3.
2024-10-17 13:45:36,562 - julearn - INFO - Using outer CV scheme RepeatedKFold(n_repeats=5, n_splits=5, random_state=200) (incl. final model)
2024-10-17 13:45:36,562 - julearn - INFO - Multi-class classification problem detected #classes = 3.
Expand Down Expand Up @@ -240,8 +240,8 @@ The scores dataframe has all the values for each CV split.
<tbody>
<tr>
<th>0</th>
<td>0.004780</td>
<td>0.002671</td>
<td>0.004622</td>
<td>0.002663</td>
<td>0.916667</td>
<td>96</td>
<td>24</td>
Expand All @@ -251,8 +251,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>1</th>
<td>0.004710</td>
<td>0.002731</td>
<td>0.004575</td>
<td>0.002630</td>
<td>0.833333</td>
<td>96</td>
<td>24</td>
Expand All @@ -262,8 +262,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>2</th>
<td>0.004713</td>
<td>0.002665</td>
<td>0.004604</td>
<td>0.002679</td>
<td>0.958333</td>
<td>96</td>
<td>24</td>
Expand All @@ -273,8 +273,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>3</th>
<td>0.004671</td>
<td>0.002753</td>
<td>0.004775</td>
<td>0.002656</td>
<td>0.916667</td>
<td>96</td>
<td>24</td>
Expand All @@ -284,8 +284,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>4</th>
<td>0.004743</td>
<td>0.002676</td>
<td>0.004589</td>
<td>0.002648</td>
<td>0.833333</td>
<td>96</td>
<td>24</td>
Expand Down Expand Up @@ -538,7 +538,7 @@ the heatmap with annotations.
.. rst-class:: sphx-glr-timing

**Total running time of the script:** (0 minutes 0.553 seconds)
**Total running time of the script:** (0 minutes 0.541 seconds)


.. _sphx_glr_download_auto_examples_00_starting_plot_cm_acc_multiclass.py:
Expand Down
84 changes: 42 additions & 42 deletions ...eview/pr-275/_sources/auto_examples/00_starting/plot_example_regression.rst.txt
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Expand Up @@ -70,13 +70,13 @@ Set the logging level to info to see extra information.
/home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead.
vstring = str(getattr(module, "__version__", None))
2024-10-17 09:50:59,638 - julearn - INFO - ===== Lib Versions =====
2024-10-17 09:50:59,638 - julearn - INFO - numpy: 1.26.4
2024-10-17 09:50:59,639 - julearn - INFO - scipy: 1.14.1
2024-10-17 09:50:59,639 - julearn - INFO - sklearn: 1.5.2
2024-10-17 09:50:59,639 - julearn - INFO - pandas: 2.2.3
2024-10-17 09:50:59,639 - julearn - INFO - julearn: 0.3.4.dev37
2024-10-17 09:50:59,639 - julearn - INFO - ========================
2024-10-17 13:45:38,271 - julearn - INFO - ===== Lib Versions =====
2024-10-17 13:45:38,271 - julearn - INFO - numpy: 1.26.4
2024-10-17 13:45:38,271 - julearn - INFO - scipy: 1.14.1
2024-10-17 13:45:38,271 - julearn - INFO - sklearn: 1.5.2
2024-10-17 13:45:38,271 - julearn - INFO - pandas: 2.2.3
2024-10-17 13:45:38,271 - julearn - INFO - julearn: 0.3.4.dev41
2024-10-17 13:45:38,272 - julearn - INFO - ========================
Expand Down Expand Up @@ -248,32 +248,32 @@ for scoring.

.. code-block:: none
2024-10-17 09:50:59,813 - julearn - INFO - ==== Input Data ====
2024-10-17 09:50:59,813 - julearn - INFO - Using dataframe as input
2024-10-17 09:50:59,813 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
2024-10-17 09:50:59,813 - julearn - INFO - Target: target
2024-10-17 09:50:59,813 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
2024-10-17 09:50:59,813 - julearn - INFO - X_types:{}
2024-10-17 09:50:59,813 - 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.
2024-10-17 13:45:38,471 - julearn - INFO - ==== Input Data ====
2024-10-17 13:45:38,471 - julearn - INFO - Using dataframe as input
2024-10-17 13:45:38,471 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
2024-10-17 13:45:38,471 - julearn - INFO - Target: target
2024-10-17 13:45:38,471 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
2024-10-17 13:45:38,471 - julearn - INFO - X_types:{}
2024-10-17 13:45:38,471 - 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(
2024-10-17 09:50:59,814 - julearn - INFO - ====================
2024-10-17 09:50:59,814 - julearn - INFO -
2024-10-17 09:50:59,814 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-10-17 09:50:59,814 - julearn - INFO - Step added
2024-10-17 09:50:59,815 - julearn - INFO - Adding step ridge that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-10-17 09:50:59,815 - julearn - INFO - Step added
2024-10-17 09:50:59,815 - julearn - INFO - = Model Parameters =
2024-10-17 09:50:59,815 - julearn - INFO - ====================
2024-10-17 09:50:59,815 - julearn - INFO -
2024-10-17 09:50:59,815 - julearn - INFO - = Data Information =
2024-10-17 09:50:59,815 - julearn - INFO - Problem type: regression
2024-10-17 09:50:59,815 - julearn - INFO - Number of samples: 309
2024-10-17 09:50:59,815 - julearn - INFO - Number of features: 10
2024-10-17 09:50:59,815 - julearn - INFO - ====================
2024-10-17 09:50:59,815 - julearn - INFO -
2024-10-17 09:50:59,816 - julearn - INFO - Target type: float64
2024-10-17 09:50:59,816 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model)
2024-10-17 13:45:38,472 - julearn - INFO - ====================
2024-10-17 13:45:38,472 - julearn - INFO -
2024-10-17 13:45:38,472 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-10-17 13:45:38,472 - julearn - INFO - Step added
2024-10-17 13:45:38,472 - julearn - INFO - Adding step ridge that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
2024-10-17 13:45:38,472 - julearn - INFO - Step added
2024-10-17 13:45:38,473 - julearn - INFO - = Model Parameters =
2024-10-17 13:45:38,473 - julearn - INFO - ====================
2024-10-17 13:45:38,473 - julearn - INFO -
2024-10-17 13:45:38,473 - julearn - INFO - = Data Information =
2024-10-17 13:45:38,473 - julearn - INFO - Problem type: regression
2024-10-17 13:45:38,473 - julearn - INFO - Number of samples: 309
2024-10-17 13:45:38,473 - julearn - INFO - Number of features: 10
2024-10-17 13:45:38,473 - julearn - INFO - ====================
2024-10-17 13:45:38,473 - julearn - INFO -
2024-10-17 13:45:38,473 - julearn - INFO - Target type: float64
2024-10-17 13:45:38,473 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model)
Expand Down Expand Up @@ -328,8 +328,8 @@ The scores dataframe has all the values for each CV split.
<tbody>
<tr>
<th>0</th>
<td>0.004877</td>
<td>0.002384</td>
<td>0.004479</td>
<td>0.002371</td>
<td>-48.783874</td>
<td>247</td>
<td>62</td>
Expand All @@ -339,8 +339,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>1</th>
<td>0.004758</td>
<td>0.002415</td>
<td>0.004429</td>
<td>0.002276</td>
<td>-47.573568</td>
<td>247</td>
<td>62</td>
Expand All @@ -350,8 +350,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>2</th>
<td>0.004754</td>
<td>0.002359</td>
<td>0.004386</td>
<td>0.002298</td>
<td>-37.617474</td>
<td>247</td>
<td>62</td>
Expand All @@ -361,8 +361,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>3</th>
<td>0.004675</td>
<td>0.002401</td>
<td>0.004386</td>
<td>0.002298</td>
<td>-47.686852</td>
<td>247</td>
<td>62</td>
Expand All @@ -372,8 +372,8 @@ The scores dataframe has all the values for each CV split.
</tr>
<tr>
<th>4</th>
<td>0.004637</td>
<td>0.002390</td>
<td>0.004408</td>
<td>0.002270</td>
<td>-45.558655</td>
<td>248</td>
<td>61</td>
Expand Down Expand Up @@ -604,7 +604,7 @@ of true values vs predicted values.
.. rst-class:: sphx-glr-timing

**Total running time of the script:** (0 minutes 0.618 seconds)
**Total running time of the script:** (0 minutes 0.673 seconds)


.. _sphx_glr_download_auto_examples_00_starting_plot_example_regression.py:
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