Deploy preview for PR 278 🛫

pr-preview/pr-278/.buildinfo

@@ -1,4 +1,4 @@
 # 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: b3018760b61dbb0c8c061103c77829f0
+config: d6c2b4367906249d27791d162c9b92f9
 tags: 645f666f9bcd5a90fca523b33c5a78b7

pr-preview/pr-278/_sources/auto_examples/00_starting/plot_cm_acc_multiclass.rst.txt

@@ -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))
-    2024-10-22 14:17:47,914 - julearn - INFO - ===== Lib Versions =====
-    2024-10-22 14:17:47,914 - julearn - INFO - numpy: 1.26.4
-    2024-10-22 14:17:47,914 - julearn - INFO - scipy: 1.14.1
-    2024-10-22 14:17:47,914 - julearn - INFO - sklearn: 1.5.2
-    2024-10-22 14:17:47,914 - julearn - INFO - pandas: 2.2.3
-    2024-10-22 14:17:47,914 - julearn - INFO - julearn: 0.3.5.dev12
-    2024-10-22 14:17:47,914 - julearn - INFO - ========================
+    2024-10-23 08:24:05,233 - julearn - INFO - ===== Lib Versions =====
+    2024-10-23 08:24:05,233 - julearn - INFO - numpy: 1.26.4
+    2024-10-23 08:24:05,233 - julearn - INFO - scipy: 1.14.1
+    2024-10-23 08:24:05,233 - julearn - INFO - sklearn: 1.5.2
+    2024-10-23 08:24:05,233 - julearn - INFO - pandas: 2.2.3
+    2024-10-23 08:24:05,233 - julearn - INFO - julearn: 0.3.5.dev16
+    2024-10-23 08:24:05,233 - julearn - INFO - ========================
 
 
 
@@ -153,39 +153,39 @@ vector machine classifier.
 
  .. code-block:: none
 
-    2024-10-22 14:17:47,918 - julearn - INFO - ==== Input Data ====
-    2024-10-22 14:17:47,918 - julearn - INFO - Using dataframe as input
-    2024-10-22 14:17:47,918 - julearn - INFO -      Features: ['sepal_length', 'sepal_width', 'petal_length']
-    2024-10-22 14:17:47,918 - julearn - INFO -      Target: species
-    2024-10-22 14:17:47,918 - julearn - INFO -      Expanded features: ['sepal_length', 'sepal_width', 'petal_length']
-    2024-10-22 14:17:47,918 - julearn - INFO -      X_types:{}
-    2024-10-22 14:17:47,918 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous.
+    2024-10-23 08:24:05,236 - julearn - INFO - ==== Input Data ====
+    2024-10-23 08:24:05,236 - julearn - INFO - Using dataframe as input
+    2024-10-23 08:24:05,236 - julearn - INFO -      Features: ['sepal_length', 'sepal_width', 'petal_length']
+    2024-10-23 08:24:05,236 - julearn - INFO -      Target: species
+    2024-10-23 08:24:05,237 - julearn - INFO -      Expanded features: ['sepal_length', 'sepal_width', 'petal_length']
+    2024-10-23 08:24:05,237 - julearn - INFO -      X_types:{}
+    2024-10-23 08:24:05,237 - 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(
-    2024-10-22 14:17:47,919 - julearn - INFO - ====================
-    2024-10-22 14:17:47,919 - julearn - INFO - 
-    2024-10-22 14:17:47,919 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-    2024-10-22 14:17:47,919 - julearn - INFO - Step added
-    2024-10-22 14:17:47,919 - julearn - INFO - Adding step svm that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-    2024-10-22 14:17:47,919 - julearn - INFO - Step added
-    2024-10-22 14:17:47,920 - julearn - INFO - = Model Parameters =
-    2024-10-22 14:17:47,920 - julearn - INFO - ====================
-    2024-10-22 14:17:47,920 - julearn - INFO - 
-    2024-10-22 14:17:47,920 - julearn - INFO - = Data Information =
-    2024-10-22 14:17:47,920 - julearn - INFO -      Problem type: classification
-    2024-10-22 14:17:47,920 - julearn - INFO -      Number of samples: 120
-    2024-10-22 14:17:47,920 - julearn - INFO -      Number of features: 3
-    2024-10-22 14:17:47,920 - julearn - INFO - ====================
-    2024-10-22 14:17:47,920 - julearn - INFO - 
-    2024-10-22 14:17:47,920 - julearn - INFO -      Number of classes: 3
-    2024-10-22 14:17:47,920 - julearn - INFO -      Target type: object
-    2024-10-22 14:17:47,921 - julearn - INFO -      Class distributions: species
+    2024-10-23 08:24:05,237 - julearn - INFO - ====================
+    2024-10-23 08:24:05,237 - julearn - INFO - 
+    2024-10-23 08:24:05,237 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+    2024-10-23 08:24:05,238 - julearn - INFO - Step added
+    2024-10-23 08:24:05,238 - julearn - INFO - Adding step svm that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+    2024-10-23 08:24:05,238 - julearn - INFO - Step added
+    2024-10-23 08:24:05,238 - julearn - INFO - = Model Parameters =
+    2024-10-23 08:24:05,238 - julearn - INFO - ====================
+    2024-10-23 08:24:05,238 - julearn - INFO - 
+    2024-10-23 08:24:05,238 - julearn - INFO - = Data Information =
+    2024-10-23 08:24:05,239 - julearn - INFO -      Problem type: classification
+    2024-10-23 08:24:05,239 - julearn - INFO -      Number of samples: 120
+    2024-10-23 08:24:05,239 - julearn - INFO -      Number of features: 3
+    2024-10-23 08:24:05,239 - julearn - INFO - ====================
+    2024-10-23 08:24:05,239 - julearn - INFO - 
+    2024-10-23 08:24:05,239 - julearn - INFO -      Number of classes: 3
+    2024-10-23 08:24:05,239 - julearn - INFO -      Target type: object
+    2024-10-23 08:24:05,239 - julearn - INFO -      Class distributions: species
     versicolor    40
     virginica     40
     setosa        40
     Name: count, dtype: int64
-    2024-10-22 14:17:47,921 - julearn - INFO - Using outer CV scheme RepeatedKFold(n_repeats=5, n_splits=5, random_state=200) (incl. final model)
-    2024-10-22 14:17:47,921 - julearn - INFO - Multi-class classification problem detected #classes = 3.
+    2024-10-23 08:24:05,240 - julearn - INFO - Using outer CV scheme RepeatedKFold(n_repeats=5, n_splits=5, random_state=200) (incl. final model)
+    2024-10-23 08:24:05,240 - julearn - INFO - Multi-class classification problem detected #classes = 3.
 
 
 
@@ -240,8 +240,8 @@ The scores dataframe has all the values for each CV split.
       <tbody>
         <tr>
           <th>0</th>
-          <td>0.004609</td>
-          <td>0.002689</td>
+          <td>0.004508</td>
+          <td>0.002610</td>
           <td>0.916667</td>
           <td>96</td>
           <td>24</td>
@@ -251,8 +251,8 @@ The scores dataframe has all the values for each CV split.
         </tr>
         <tr>
           <th>1</th>
-          <td>0.004572</td>
-          <td>0.002673</td>
+          <td>0.004402</td>
+          <td>0.002580</td>
           <td>0.833333</td>
           <td>96</td>
           <td>24</td>
@@ -262,8 +262,8 @@ The scores dataframe has all the values for each CV split.
         </tr>
         <tr>
           <th>2</th>
-          <td>0.004525</td>
-          <td>0.002661</td>
+          <td>0.004396</td>
+          <td>0.002541</td>
           <td>0.958333</td>
           <td>96</td>
           <td>24</td>
@@ -273,8 +273,8 @@ The scores dataframe has all the values for each CV split.
         </tr>
         <tr>
           <th>3</th>
-          <td>0.004505</td>
-          <td>0.002587</td>
+          <td>0.004418</td>
+          <td>0.002561</td>
           <td>0.916667</td>
           <td>96</td>
           <td>24</td>
@@ -284,8 +284,8 @@ The scores dataframe has all the values for each CV split.
         </tr>
         <tr>
           <th>4</th>
-          <td>0.004455</td>
-          <td>0.002601</td>
+          <td>0.004368</td>
+          <td>0.002528</td>
           <td>0.833333</td>
           <td>96</td>
           <td>24</td>
@@ -538,7 +538,7 @@ the heatmap with annotations.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.476 seconds)
+   **Total running time of the script:** (0 minutes 0.531 seconds)
 
 
 .. _sphx_glr_download_auto_examples_00_starting_plot_cm_acc_multiclass.py:

pr-preview/pr-278/_sources/auto_examples/00_starting/plot_example_regression.rst.txt

@@ -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))
-    2024-10-22 14:17:49,382 - julearn - INFO - ===== Lib Versions =====
-    2024-10-22 14:17:49,382 - julearn - INFO - numpy: 1.26.4
-    2024-10-22 14:17:49,382 - julearn - INFO - scipy: 1.14.1
-    2024-10-22 14:17:49,382 - julearn - INFO - sklearn: 1.5.2
-    2024-10-22 14:17:49,382 - julearn - INFO - pandas: 2.2.3
-    2024-10-22 14:17:49,382 - julearn - INFO - julearn: 0.3.5.dev12
-    2024-10-22 14:17:49,382 - julearn - INFO - ========================
+    2024-10-23 08:24:06,905 - julearn - INFO - ===== Lib Versions =====
+    2024-10-23 08:24:06,905 - julearn - INFO - numpy: 1.26.4
+    2024-10-23 08:24:06,905 - julearn - INFO - scipy: 1.14.1
+    2024-10-23 08:24:06,905 - julearn - INFO - sklearn: 1.5.2
+    2024-10-23 08:24:06,905 - julearn - INFO - pandas: 2.2.3
+    2024-10-23 08:24:06,905 - julearn - INFO - julearn: 0.3.5.dev16
+    2024-10-23 08:24:06,905 - julearn - INFO - ========================
 
 
 
@@ -248,32 +248,32 @@ for scoring.
 
  .. code-block:: none
 
-    2024-10-22 14:17:49,549 - julearn - INFO - ==== Input Data ====
-    2024-10-22 14:17:49,549 - julearn - INFO - Using dataframe as input
-    2024-10-22 14:17:49,549 - julearn - INFO -      Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
-    2024-10-22 14:17:49,549 - julearn - INFO -      Target: target
-    2024-10-22 14:17:49,549 - julearn - INFO -      Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
-    2024-10-22 14:17:49,549 - julearn - INFO -      X_types:{}
-    2024-10-22 14:17:49,549 - 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.
+    2024-10-23 08:24:07,103 - julearn - INFO - ==== Input Data ====
+    2024-10-23 08:24:07,103 - julearn - INFO - Using dataframe as input
+    2024-10-23 08:24:07,103 - julearn - INFO -      Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
+    2024-10-23 08:24:07,103 - julearn - INFO -      Target: target
+    2024-10-23 08:24:07,103 - julearn - INFO -      Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
+    2024-10-23 08:24:07,103 - julearn - INFO -      X_types:{}
+    2024-10-23 08:24:07,103 - 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(
-    2024-10-22 14:17:49,550 - julearn - INFO - ====================
-    2024-10-22 14:17:49,550 - julearn - INFO - 
-    2024-10-22 14:17:49,550 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-    2024-10-22 14:17:49,550 - julearn - INFO - Step added
-    2024-10-22 14:17:49,551 - julearn - INFO - Adding step ridge that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-    2024-10-22 14:17:49,551 - julearn - INFO - Step added
-    2024-10-22 14:17:49,551 - julearn - INFO - = Model Parameters =
-    2024-10-22 14:17:49,551 - julearn - INFO - ====================
-    2024-10-22 14:17:49,551 - julearn - INFO - 
-    2024-10-22 14:17:49,551 - julearn - INFO - = Data Information =
-    2024-10-22 14:17:49,551 - julearn - INFO -      Problem type: regression
-    2024-10-22 14:17:49,551 - julearn - INFO -      Number of samples: 309
-    2024-10-22 14:17:49,551 - julearn - INFO -      Number of features: 10
-    2024-10-22 14:17:49,551 - julearn - INFO - ====================
-    2024-10-22 14:17:49,551 - julearn - INFO - 
-    2024-10-22 14:17:49,552 - julearn - INFO -      Target type: float64
-    2024-10-22 14:17:49,552 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model)
+    2024-10-23 08:24:07,104 - julearn - INFO - ====================
+    2024-10-23 08:24:07,104 - julearn - INFO - 
+    2024-10-23 08:24:07,104 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+    2024-10-23 08:24:07,104 - julearn - INFO - Step added
+    2024-10-23 08:24:07,104 - julearn - INFO - Adding step ridge that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+    2024-10-23 08:24:07,104 - julearn - INFO - Step added
+    2024-10-23 08:24:07,105 - julearn - INFO - = Model Parameters =
+    2024-10-23 08:24:07,105 - julearn - INFO - ====================
+    2024-10-23 08:24:07,105 - julearn - INFO - 
+    2024-10-23 08:24:07,105 - julearn - INFO - = Data Information =
+    2024-10-23 08:24:07,105 - julearn - INFO -      Problem type: regression
+    2024-10-23 08:24:07,105 - julearn - INFO -      Number of samples: 309
+    2024-10-23 08:24:07,105 - julearn - INFO -      Number of features: 10
+    2024-10-23 08:24:07,105 - julearn - INFO - ====================
+    2024-10-23 08:24:07,105 - julearn - INFO - 
+    2024-10-23 08:24:07,105 - julearn - INFO -      Target type: float64
+    2024-10-23 08:24:07,106 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model)
 
 
 
@@ -328,8 +328,8 @@ The scores dataframe has all the values for each CV split.
       <tbody>
         <tr>
           <th>0</th>
-          <td>0.004641</td>
-          <td>0.002336</td>
+          <td>0.004495</td>
+          <td>0.002264</td>
           <td>-48.783874</td>
           <td>247</td>
           <td>62</td>
@@ -339,8 +339,8 @@ The scores dataframe has all the values for each CV split.
         </tr>
         <tr>
           <th>1</th>
-          <td>0.004584</td>
-          <td>0.002337</td>
+          <td>0.004429</td>
+          <td>0.002280</td>
           <td>-47.573568</td>
           <td>247</td>
           <td>62</td>
@@ -350,8 +350,8 @@ The scores dataframe has all the values for each CV split.
         </tr>
         <tr>
           <th>2</th>
-          <td>0.004480</td>
-          <td>0.002329</td>
+          <td>0.004368</td>
+          <td>0.002279</td>
           <td>-37.617474</td>
           <td>247</td>
           <td>62</td>
@@ -361,8 +361,8 @@ The scores dataframe has all the values for each CV split.
         </tr>
         <tr>
           <th>3</th>
-          <td>0.004459</td>
-          <td>0.002358</td>
+          <td>0.004359</td>
+          <td>0.002266</td>
           <td>-47.686852</td>
           <td>247</td>
           <td>62</td>
@@ -372,8 +372,8 @@ The scores dataframe has all the values for each CV split.
         </tr>
         <tr>
           <th>4</th>
-          <td>0.004462</td>
-          <td>0.002645</td>
+          <td>0.004399</td>
+          <td>0.002260</td>
           <td>-45.558655</td>
           <td>248</td>
           <td>61</td>
@@ -604,7 +604,7 @@ of true values vs predicted values.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.543 seconds)
+   **Total running time of the script:** (0 minutes 0.667 seconds)
 
 
 .. _sphx_glr_download_auto_examples_00_starting_plot_example_regression.py: