Update documentation

docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt

@@ -542,9 +542,9 @@ randomization.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 0.085 seconds)
+   **Total running time of the script:** (1 minutes 5.057 seconds)
 
-**Estimated memory usage:**  99 MB
+**Estimated memory usage:**  101 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_2D_simulation_example.py:

docs/_sources/auto_examples/plot_diabetes_variable_importance_example.rst.txt

@@ -194,9 +194,9 @@ Plotting the comparison
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 46.171 seconds)
+   **Total running time of the script:** (0 minutes 47.274 seconds)
 
-**Estimated memory usage:**  28 MB
+**Estimated memory usage:**  29 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_diabetes_variable_importance_example.py:

docs/_sources/auto_examples/plot_fmri_data_example.rst.txt

@@ -184,7 +184,7 @@ You may choose a subject in [1, 2, 3, 4, 5, 6]. By default subject=2.
     Downloading data from http://data.pymvpa.org/datasets/haxby2001/MD5SUMS ...
      ...done. (0 seconds, 0 min)
     Downloading data from http://data.pymvpa.org/datasets/haxby2001/subj2-2010.01.14.tar.gz ...
-    Downloaded 20447232 of 291168628 bytes (7.0%,   13.6s remaining)    Downloaded 62308352 of 291168628 bytes (21.4%,    7.6s remaining)    Downloaded 104996864 of 291168628 bytes (36.1%,    5.5s remaining)    Downloaded 147136512 of 291168628 bytes (50.5%,    4.0s remaining)    Downloaded 189079552 of 291168628 bytes (64.9%,    2.8s remaining)    Downloaded 231620608 of 291168628 bytes (79.5%,    1.6s remaining)    Downloaded 273883136 of 291168628 bytes (94.1%,    0.5s remaining) ...done. (8 seconds, 0 min)
+    Downloaded 109805568 of 291168628 bytes (37.7%,    1.7s remaining)    Downloaded 240345088 of 291168628 bytes (82.5%,    0.4s remaining) ...done. (3 seconds, 0 min)
     Extracting data from /home/runner/nilearn_data/haxby2001/def37a305edfda829916fa14c9ea08f8/subj2-2010.01.14.tar.gz..... done.
 
 
@@ -281,8 +281,8 @@ Now, we compute p-values thanks to permutation tests applied to
 
  .. code-block:: none
 
-    [Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    1.6s
-    [Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    6.6s
+    [Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    1.7s
+    [Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    6.4s
 
 
 
@@ -361,7 +361,7 @@ However you might benefit from clustering randomization taking
  .. code-block:: none
 
     [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
-    [Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   31.3s finished
+    [Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   33.5s finished
 
 
 
@@ -599,9 +599,9 @@ spurious discoveries.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 26.859 seconds)
+   **Total running time of the script:** (1 minutes 26.070 seconds)
 
-**Estimated memory usage:**  2762 MB
+**Estimated memory usage:**  2790 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_fmri_data_example.py:

docs/_sources/auto_examples/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**03:13.114** total execution time for 3 files **from auto_examples**:
+**03:18.400** total execution time for 3 files **from auto_examples**:
 
 .. container::
 
@@ -33,11 +33,11 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py` (``plot_fmri_data_example.py``)
-     - 01:26.859
-     - 2762.1
+     - 01:26.070
+     - 2789.9
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``plot_2D_simulation_example.py``)
-     - 01:00.085
-     - 98.6
+     - 01:05.057
+     - 100.9
    * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``plot_diabetes_variable_importance_example.py``)
-     - 00:46.171
-     - 28.4
+     - 00:47.274
+     - 28.9

docs/_sources/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**03:13.114** total execution time for 3 files **from all galleries**:
+**03:18.400** total execution time for 3 files **from all galleries**:
 
 .. container::
 
@@ -33,11 +33,11 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py` (``../examples/plot_fmri_data_example.py``)
-     - 01:26.859
-     - 2762.1
+     - 01:26.070
+     - 2789.9
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``../examples/plot_2D_simulation_example.py``)
-     - 01:00.085
-     - 98.6
+     - 01:05.057
+     - 100.9
    * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``../examples/plot_diabetes_variable_importance_example.py``)
-     - 00:46.171
-     - 28.4
+     - 00:47.274
+     - 28.9

docs/api.html

@@ -138,7 +138,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Link to this heading
 <td><p>Aggregation of survival function values by adaptive quantile procedure</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/hidimstat.BlockBasedImportance.html#hidimstat.BlockBasedImportance" title="hidimstat.BlockBasedImportance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BlockBasedImportance</span></code></a>([estimator, ...])</p></td>
-<td><p>This class implements the Block-Based Importance (BBI), consisting of a learner block (first block) and an importance block (second block). For single-level see <a href="#id3"><span class="problematic" id="id4">:footcite:t:`Chamma_NeurIPS2023`</span></a> and for group-level see <a href="#id5"><span class="problematic" id="id6">:footcite:t:`Chamma_AAAI2024`</span></a>. Parameters ---------- estimator: scikit-learn compatible estimator, default=None     The provided estimator for the prediction task (First block). The default estimator is the DNN learner. Other options are (1) RF     for Random Forest. importance_estimator: {scikit-learn compatible estimator or string},                       default=Mod_RF     The provided estimator for the importance task (Second block). Using &quot;Mod_RF&quot; will apply the modified version of the Random Forest as     the importance predictor. do_hypertuning: bool, default=True     Tuning the hyperparameters of the provided estimator. dict_hypertuning: dict, default=None     The dictionary of hyperparameters to tune. problem_type: str, default='regression'     A classification or a regression problem. bootstrap: bool, default=True     Application of bootstrap sampling for the training set. split_perc: float, default=0.8     The training/validation cut for the provided data. conditional: bool, default=True     The permutation or the conditional sampling approach. list_nominal: dict, default=None     The dictionary of binary, nominal and ordinal variables. Perm: bool, default=False     The use of permutations or random sampling with CPI-DNN. n_perm: int, default=50     The number of permutations/random sampling for each column. n_jobs: int, default=1     The number of workers for parallel processing. verbose: int, default=0     If verbose &gt; 0, the fitted iterations will be printed. groups: dict, default=None     The knowledge-driven/data-driven grouping of the variables if provided. group_stacking: bool, default=False     Apply the stacking-based method for the provided groups. prop_out_subLayers: int, default=0. If group_stacking is set to True, proportion of outputs for     the linear sub-layers per group. index_i: int, default=None     The index of the current processed iteration. random_state: int, default=2023     Fixing the seeds of the random generator. com_imp: boolean, default=True     Compute or not the importance scores. group_label: list, default=None     The list of group labels to perform GroupKFold Attributes ---------- ToDO.</p></td>
+<td><p>This class implements the Block-Based Importance (BBI), consisting of a learner block (first block) and an importance block (second block). For single-level see CHAMMA <em>et al.</em><a href="#id6"><span class="problematic" id="id3">[#Chamma_NeurIPS2023]_</span></a> and for group-level see Chamma <em>et al.</em><a href="#id7"><span class="problematic" id="id4">[#Chamma_AAAI2024]_</span></a>. Parameters ---------- estimator: scikit-learn compatible estimator, default=None     The provided estimator for the prediction task (First block). The default estimator is the DNN learner. Other options are (1) RF     for Random Forest. importance_estimator: {scikit-learn compatible estimator or string},                       default=Mod_RF     The provided estimator for the importance task (Second block). Using &quot;Mod_RF&quot; will apply the modified version of the Random Forest as     the importance predictor. do_hypertuning: bool, default=True     Tuning the hyperparameters of the provided estimator. dict_hypertuning: dict, default=None     The dictionary of hyperparameters to tune. problem_type: str, default='regression'     A classification or a regression problem. bootstrap: bool, default=True     Application of bootstrap sampling for the training set. split_perc: float, default=0.8     The training/validation cut for the provided data. conditional: bool, default=True     The permutation or the conditional sampling approach. list_nominal: dict, default=None     The dictionary of binary, nominal and ordinal variables. Perm: bool, default=False     The use of permutations or random sampling with CPI-DNN. n_perm: int, default=50     The number of permutations/random sampling for each column. n_jobs: int, default=1     The number of workers for parallel processing. verbose: int, default=0     If verbose &gt; 0, the fitted iterations will be printed. groups: dict, default=None     The knowledge-driven/data-driven grouping of the variables if provided. group_stacking: bool, default=False     Apply the stacking-based method for the provided groups. prop_out_subLayers: int, default=0. If group_stacking is set to True, proportion of outputs for     the linear sub-layers per group. index_i: int, default=None     The index of the current processed iteration. random_state: int, default=2023     Fixing the seeds of the random generator. com_imp: boolean, default=True     Compute or not the importance scores. group_label: list, default=None     The list of group labels to perform GroupKFold Attributes ---------- ToDO.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.clustered_inference.html#hidimstat.clustered_inference" title="hidimstat.clustered_inference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">clustered_inference</span></code></a>(X_init, y, ward, n_clusters)</p></td>
 <td><p>Clustered inference algorithm</p></td>

docs/auto_examples/plot_2D_simulation_example.html

@@ -473,8 +473,8 @@ <h2>Analysis of the results<a class="headerlink" href="#analysis-of-the-results"
 conservative. In practice, Type-1 Error guarantees seem to hold
 for a lower spatial tolerance. This is an additional benefit of clustering
 randomization.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 0.085 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  99 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 5.057 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  101 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-2d-simulation-example-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/7d2770a07fbe419760c9ac177df4f69e/plot_2D_simulation_example.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_2D_simulation_example.ipynb</span></code></a></p>

docs/auto_examples/plot_diabetes_variable_importance_example.html

@@ -227,8 +227,8 @@ <h2>Plotting the comparison<a class="headerlink" href="#plotting-the-comparison"
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_diabetes_variable_importance_example_001.png" srcset="../_images/sphx_glr_plot_diabetes_variable_importance_example_001.png" alt="plot diabetes variable importance example" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 46.171 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  28 MB</p>
+<img src="../_images/sphx_glr_plot_diabetes_variable_importance_example_001.png" srcset="../_images/sphx_glr_plot_diabetes_variable_importance_example_001.png" alt="plot diabetes variable importance example" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 47.274 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  29 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-diabetes-variable-importance-example-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/a70e28075a283d5e3fe675ced733c459/plot_diabetes_variable_importance_example.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_diabetes_variable_importance_example.ipynb</span></code></a></p>

docs/auto_examples/plot_fmri_data_example.html

@@ -252,13 +252,8 @@ <h2>Gathering and preprocessing Haxby dataset for a given subject<a class="heade
  ...done. (0 seconds, 0 min)
 Downloading data from http://data.pymvpa.org/datasets/haxby2001/subj2-2010.01.14.tar.gz ...
 
-Downloaded 20447232 of 291168628 bytes (7.0%,   13.6s remaining)
-Downloaded 62308352 of 291168628 bytes (21.4%,    7.6s remaining)
-Downloaded 104996864 of 291168628 bytes (36.1%,    5.5s remaining)
-Downloaded 147136512 of 291168628 bytes (50.5%,    4.0s remaining)
-Downloaded 189079552 of 291168628 bytes (64.9%,    2.8s remaining)
-Downloaded 231620608 of 291168628 bytes (79.5%,    1.6s remaining)
-Downloaded 273883136 of 291168628 bytes (94.1%,    0.5s remaining) ...done. (8 seconds, 0 min)
+Downloaded 109805568 of 291168628 bytes (37.7%,    1.7s remaining)
+Downloaded 240345088 of 291168628 bytes (82.5%,    0.4s remaining) ...done. (3 seconds, 0 min)
 Extracting data from /home/runner/nilearn_data/haxby2001/def37a305edfda829916fa14c9ea08f8/subj2-2010.01.14.tar.gz..... done.
 </pre></div>
 </div>
@@ -309,8 +304,8 @@ <h2>Making the inference with several algorithms<a class="headerlink" href="#mak
 <span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    1.6s
-[Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    6.6s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    1.7s
+[Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    6.4s
 </pre></div>
 </div>
 <p>Now, let us run the algorithm introduced by Gaonkar et al. (c.f. References).
@@ -344,7 +339,7 @@ <h2>Making the inference with several algorithms<a class="headerlink" href="#mak
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
-[Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   31.3s finished
+[Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   33.5s finished
 </pre></div>
 </div>
 </section>
@@ -463,8 +458,8 @@ <h2>Analysis of the results<a class="headerlink" href="#analysis-of-the-results"
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 26.859 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  2762 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 26.070 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  2790 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-fmri-data-example-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/931385a6992917f918857d6a3ee9f780/plot_fmri_data_example.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_fmri_data_example.ipynb</span></code></a></p>

docs/auto_examples/sg_execution_times.html

@@ -110,7 +110,7 @@
 
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">¶</a></h1>
-<p><strong>03:13.114</strong> total execution time for 3 files <strong>from auto_examples</strong>:</p>
+<p><strong>03:18.400</strong> total execution time for 3 files <strong>from auto_examples</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -132,16 +132,16 @@
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_fmri_data_example.html#sphx-glr-auto-examples-plot-fmri-data-example-py"><span class="std std-ref">Support recovery on fMRI data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_fmri_data_example.py</span></code>)</p></td>
-<td><p>01:26.859</p></td>
-<td><p>2762.1</p></td>
+<td><p>01:26.070</p></td>
+<td><p>2789.9</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_2D_simulation_example.html#sphx-glr-auto-examples-plot-2d-simulation-example-py"><span class="std std-ref">Support recovery on simulated data (2D)</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_2D_simulation_example.py</span></code>)</p></td>
-<td><p>01:00.085</p></td>
-<td><p>98.6</p></td>
+<td><p>01:05.057</p></td>
+<td><p>100.9</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_diabetes_variable_importance_example.html#sphx-glr-auto-examples-plot-diabetes-variable-importance-example-py"><span class="std std-ref">Variable Importance on diabetes dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_diabetes_variable_importance_example.py</span></code>)</p></td>
-<td><p>00:46.171</p></td>
-<td><p>28.4</p></td>
+<td><p>00:47.274</p></td>
+<td><p>28.9</p></td>
 </tr>
 </tbody>
 </table>

docs/generated/hidimstat.BlockBasedImportance.html

@@ -132,8 +132,8 @@ <h1>hidimstat.BlockBasedImportance<a class="headerlink" href="#hidimstat-blockba
 <dd><p>This class implements the Block-Based Importance (BBI),
 consisting of a learner block (first block) and an importance block (second
 block).
-For single-level see <a href="#id1"><span class="problematic" id="id2">:footcite:t:`Chamma_NeurIPS2023`</span></a> and for group-level
-see <a href="#id3"><span class="problematic" id="id4">:footcite:t:`Chamma_AAAI2024`</span></a>.
+For single-level see CHAMMA <em>et al.</em><a class="footnote-reference brackets" href="#footcite-chamma-neurips2023" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> and for group-level
+see Chamma <em>et al.</em><a class="footnote-reference brackets" href="#footcite-chamma-aaai2024" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.
 Parameters
 ———-
 estimator: scikit-learn compatible estimator, default=None</p>