Pushing the docs to main/ for branch: main, commit 101568b543c2db57df…

…7bc826c5a817cb55a42b57

main/.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: 6d2c45670a665faa7ed267e976a47e6f
+config: 4c4e45c5ef9144f9491b1ec054f9e1d7
 tags: 645f666f9bcd5a90fca523b33c5a78b7

main/_sources/auto_examples/plot_adversarial_basics.rst.txt

@@ -621,14 +621,14 @@ Then, we can fit the data to our model:
                                                                        SimpleImputer()),
                                                                       ('normalizer',
                                                                        StandardScaler())]),
-                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7f3bacc09820>),
+                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7f90a9723dd0>),
                                                      ('pipeline-2',
                                                       Pipeline(steps=[('imputer',
                                                                        SimpleImputer(strategy='most_frequent')),
                                                                       ('encoder',
                                                                        OneHotEncoder(drop='if_binary',
                                                                                      sparse_output=False))]),
-                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7f3bacc0ba40>)])),
+                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7f90a9b16e40>)])),
                     ('adversarialfairnessclassifier',
                      AdversarialFairnessClassifier(adversary_model=[3,
                                                                     'leaky_relu'],
@@ -642,14 +642,14 @@ Then, we can fit the data to our model:
                                                                        SimpleImputer()),
                                                                       ('normalizer',
                                                                        StandardScaler())]),
-                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7f3bacc09820>),
+                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7f90a9723dd0>),
                                                      ('pipeline-2',
                                                       Pipeline(steps=[('imputer',
                                                                        SimpleImputer(strategy='most_frequent')),
                                                                       ('encoder',
                                                                        OneHotEncoder(drop='if_binary',
                                                                                      sparse_output=False))]),
-                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7f3bacc0ba40>)])),
+                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7f90a9b16e40>)])),
                     ('adversarialfairnessclassifier',
                      AdversarialFairnessClassifier(adversary_model=[3,
                                                                     'leaky_relu'],
@@ -661,14 +661,14 @@ Then, we can fit the data to our model:
                                      Pipeline(steps=[('imputer', SimpleImputer()),
                                                      ('normalizer',
                                                       StandardScaler())]),
-                                     <sklearn.compose._column_transformer.make_column_selector object at 0x7f3bacc09820>),
+                                     <sklearn.compose._column_transformer.make_column_selector object at 0x7f90a9723dd0>),
                                     ('pipeline-2',
                                      Pipeline(steps=[('imputer',
                                                       SimpleImputer(strategy='most_frequent')),
                                                      ('encoder',
                                                       OneHotEncoder(drop='if_binary',
                                                                     sparse_output=False))]),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f3bacc0ba40&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">pipeline-1</label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f3bacc09820&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;StandardScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">pipeline-2</label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f3bacc0ba40&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;OneHotEncoder<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">AdversarialFairnessClassifier</label><div class="sk-toggleable__content fitted"><pre>AdversarialFairnessClassifier(adversary_model=[3, &#x27;leaky_relu&#x27;],
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f90a9b16e40&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">pipeline-1</label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f90a9723dd0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;StandardScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">pipeline-2</label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f90a9b16e40&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;OneHotEncoder<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">AdversarialFairnessClassifier</label><div class="sk-toggleable__content fitted"><pre>AdversarialFairnessClassifier(adversary_model=[3, &#x27;leaky_relu&#x27;],
                                   backend=&#x27;torch&#x27;, batch_size=256,
                                   predictor_model=[50, &#x27;leaky_relu&#x27;],
                                   progress_updates=0.5, random_state=123)</pre></div> </div></div></div></div></div></div>
@@ -749,7 +749,7 @@ Then, to display the result:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 12.065 seconds)
+   **Total running time of the script:** (0 minutes 10.792 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_adversarial_basics.py:

main/_sources/auto_examples/plot_adversarial_fine_tuning.rst.txt

@@ -476,7 +476,7 @@ Finally, we validate as before, and take a look at the results:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (3 minutes 51.738 seconds)
+   **Total running time of the script:** (9 minutes 46.473 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_adversarial_fine_tuning.py:

main/_sources/auto_examples/plot_correlationremover_before_after.rst.txt

@@ -226,7 +226,7 @@ not all the correlation being projected away.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.262 seconds)
+   **Total running time of the script:** (0 minutes 1.386 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_correlationremover_before_after.py:

main/_sources/auto_examples/plot_credit_loan_decisions.rst.txt

@@ -597,7 +597,7 @@ individuals who defaulted on their loan.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7f3b82351be0>
+    <matplotlib.legend.Legend object at 0x7f907fff4560>
 
 
 
@@ -2221,7 +2221,7 @@ Now, let's plot the performance trade-offs between all of our models.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7f3b805cf4d0>
+    <matplotlib.legend.Legend object at 0x7f906814dee0>
 
 
 
@@ -2638,7 +2638,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 40.974 seconds)
+   **Total running time of the script:** (0 minutes 34.652 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_credit_loan_decisions.py:

main/_sources/auto_examples/plot_grid_search_census.rst.txt

@@ -1109,7 +1109,7 @@ Fairlearn also provides functionality to compare models much more easily.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 25.788 seconds)
+   **Total running time of the script:** (1 minutes 41.493 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_grid_search_census.py:

main/_sources/auto_examples/plot_intersectional_bias.rst.txt

@@ -1462,7 +1462,7 @@ frequency of diagnoses, stratified by intersectional groups.
  .. code-block:: none
 
 
-    <seaborn.axisgrid.FacetGrid object at 0x7f3b82360c50>
+    <seaborn.axisgrid.FacetGrid object at 0x7f907fde1640>
 
 
 
@@ -1858,83 +1858,83 @@ Gets the weights associated with each feature, and scales them from 0-100
         </tr>
         <tr>
           <th>Housing_Unstable</th>
-          <td>90.856692</td>
+          <td>90.857652</td>
         </tr>
         <tr>
           <th>Tension</th>
-          <td>87.330737</td>
+          <td>87.331216</td>
         </tr>
         <tr>
           <th>Race_White</th>
-          <td>82.565611</td>
+          <td>82.567431</td>
         </tr>
         <tr>
           <th>Delusion</th>
-          <td>55.671726</td>
+          <td>55.672176</td>
         </tr>
         <tr>
           <th>Unusual_Thought</th>
-          <td>53.261582</td>
+          <td>53.262101</td>
         </tr>
         <tr>
           <th>Suspicious</th>
-          <td>45.993972</td>
+          <td>45.994430</td>
         </tr>
         <tr>
           <th>Concentration</th>
-          <td>45.473402</td>
+          <td>45.474084</td>
         </tr>
         <tr>
           <th>Anhedonia</th>
-          <td>41.976557</td>
+          <td>41.976147</td>
         </tr>
         <tr>
           <th>Tired</th>
-          <td>41.872949</td>
+          <td>41.872551</td>
         </tr>
         <tr>
           <th>Delay_Yes</th>
-          <td>41.833546</td>
+          <td>41.833977</td>
         </tr>
         <tr>
           <th>Sex_Female</th>
-          <td>38.435778</td>
+          <td>38.435499</td>
         </tr>
         <tr>
           <th>Psychomotor</th>
-          <td>29.893131</td>
+          <td>29.892799</td>
         </tr>
         <tr>
           <th>Dep_Mood</th>
-          <td>26.117527</td>
+          <td>26.117165</td>
         </tr>
         <tr>
           <th>Withdrawal</th>
-          <td>18.919229</td>
+          <td>18.919552</td>
         </tr>
         <tr>
           <th>Race_Black</th>
-          <td>10.112582</td>
+          <td>10.113795</td>
         </tr>
         <tr>
           <th>Race_Asian</th>
-          <td>5.350475</td>
+          <td>5.349429</td>
         </tr>
         <tr>
           <th>Sleep</th>
-          <td>5.186373</td>
+          <td>5.186974</td>
         </tr>
         <tr>
           <th>Housing_Stable</th>
-          <td>2.549439</td>
+          <td>2.547355</td>
         </tr>
         <tr>
           <th>Passive</th>
-          <td>0.532121</td>
+          <td>0.532522</td>
         </tr>
         <tr>
           <th>Appetite</th>
-          <td>0.057554</td>
+          <td>0.057089</td>
         </tr>
         <tr>
           <th>Sex_Male</th>
@@ -2158,7 +2158,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.356 seconds)
+   **Total running time of the script:** (0 minutes 5.170 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_intersectional_bias.py:

main/_sources/auto_examples/plot_make_derived_metric.rst.txt

@@ -347,8 +347,8 @@ To show that the returned function also works with sample weights:
 
  .. code-block:: none
 
-    From MetricFrame: 0.8087088961029228
-    From function   : 0.8087088961029228
+    From MetricFrame: 0.8073348420940396
+    From function   : 0.8073348420940396
 
 
 
@@ -388,7 +388,7 @@ First we evaluate this with a :class:`fairlearn.metrics.MetricFrame`:
 
  .. code-block:: none
 
-    From frame: 0.026129952916922994
+    From frame: 0.015416737256824575
 
 
 
@@ -426,7 +426,7 @@ we do not need to use :func:`functools.partial` to bind the
 
  .. code-block:: none
 
-    From function: 0.026129952916922994
+    From function: 0.015416737256824575
 
 
 
@@ -471,7 +471,7 @@ find the minimum over the accuracy scores:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.874 seconds)
+   **Total running time of the script:** (0 minutes 1.122 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_make_derived_metric.py:

main/_sources/auto_examples/plot_metricframe_beyond_binary_classification.rst.txt

@@ -518,7 +518,7 @@ work).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.084 seconds)
+   **Total running time of the script:** (0 minutes 0.098 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_metricframe_beyond_binary_classification.py:

main/_sources/auto_examples/plot_mitigation_pipeline.rst.txt

@@ -234,7 +234,7 @@ using the step name followed by two underscores and :code:`sample_weight`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 13.083 seconds)
+   **Total running time of the script:** (0 minutes 16.077 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_mitigation_pipeline.py: