Update documentation

docs/_sources/api.rst.txt

@@ -17,6 +17,7 @@ Functions
 
    ada_svr
    aggregate_quantiles
+   BlockBasedImportance
    clustered_inference
    desparsified_lasso
    ensemble_clustered_inference

docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt

@@ -542,7 +542,7 @@ randomization.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 8.829 seconds)
+   **Total running time of the script:** (1 minutes 9.270 seconds)
 
 **Estimated memory usage:**  101 MB
 

docs/_sources/auto_examples/plot_diabetesFeatures_importance_example.rst.txt

@@ -194,9 +194,9 @@ Plotting the comparison
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 46.623 seconds)
+   **Total running time of the script:** (0 minutes 47.285 seconds)
 
-**Estimated memory usage:**  29 MB
+**Estimated memory usage:**  32 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_diabetesFeatures_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 99262464 of 291168628 bytes (34.1%,    1.9s remaining)    Downloaded 216768512 of 291168628 bytes (74.4%,    0.7s remaining) ...done. (3 seconds, 0 min)
+    Downloaded 107290624 of 291168628 bytes (36.8%,    1.7s remaining)    Downloaded 238895104 of 291168628 bytes (82.0%,    0.4s remaining) ...done. (3 seconds, 0 min)
     Extracting data from /home/runner/nilearn_data/haxby2001/def37a305edfda829916fa14c9ea08f8/subj2-2010.01.14.tar.gz..... done.
 
 
@@ -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:   32.3s finished
+    [Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   32.6s finished
 
 
 
@@ -599,9 +599,9 @@ spurious discoveries.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 24.514 seconds)
+   **Total running time of the script:** (1 minutes 25.149 seconds)
 
-**Estimated memory usage:**  2679 MB
+**Estimated memory usage:**  2772 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
 =================
-**03:19.966** total execution time for 3 files **from auto_examples**:
+**03:21.704** 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``)
-     - 01:24.514
-     - 2678.7
+     - 01:25.149
+     - 2772.0
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``plot_2D_simulation_example.py``)
-     - 01:08.829
-     - 101.3
+     - 01:09.270
+     - 100.8
    * - :ref:`sphx_glr_auto_examples_plot_diabetesFeatures_importance_example.py` (``plot_diabetesFeatures_importance_example.py``)
-     - 00:46.623
-     - 28.8
+     - 00:47.285
+     - 31.5

docs/_sources/generated/hidimstat.BlockBasedImportance.rst.txt

@@ -0,0 +1,33 @@
+hidimstat.BlockBasedImportance
+==============================
+
+.. currentmodule:: hidimstat
+
+.. autoclass:: BlockBasedImportance
+
+
+   .. automethod:: __init__
+
+
+   .. rubric:: Methods
+
+   .. autosummary::
+
+      ~BlockBasedImportance.__init__
+      ~BlockBasedImportance.compute_importance
+      ~BlockBasedImportance.fit
+      ~BlockBasedImportance.fit_transform
+      ~BlockBasedImportance.get_metadata_routing
+      ~BlockBasedImportance.get_params
+      ~BlockBasedImportance.predict
+      ~BlockBasedImportance.predict_proba
+      ~BlockBasedImportance.set_output
+      ~BlockBasedImportance.set_params
+      ~BlockBasedImportance.set_predict_proba_request
+      ~BlockBasedImportance.set_predict_request
+
+
+
+
+
+

docs/_sources/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**03:19.966** total execution time for 3 files **from all galleries**:
+**03:21.704** 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``)
-     - 01:24.514
-     - 2678.7
+     - 01:25.149
+     - 2772.0
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``../examples/plot_2D_simulation_example.py``)
-     - 01:08.829
-     - 101.3
+     - 01:09.270
+     - 100.8
    * - :ref:`sphx_glr_auto_examples_plot_diabetesFeatures_importance_example.py` (``../examples/plot_diabetesFeatures_importance_example.py``)
-     - 00:46.623
-     - 28.8
+     - 00:47.285
+     - 31.5

docs/api.html

@@ -137,34 +137,37 @@ <h2>Functions<a class="headerlink" href="#functions" title="Link to this heading
 <tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.aggregate_quantiles.html#hidimstat.aggregate_quantiles" title="hidimstat.aggregate_quantiles"><code class="xref py py-obj docutils literal notranslate"><span class="pre">aggregate_quantiles</span></code></a>(list_one_sided_pval[, ...])</p></td>
 <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.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>
+<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),</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>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.desparsified_lasso.html#hidimstat.desparsified_lasso" title="hidimstat.desparsified_lasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">desparsified_lasso</span></code></a>(X, y[, dof_ajdustement, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/hidimstat.desparsified_lasso.html#hidimstat.desparsified_lasso" title="hidimstat.desparsified_lasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">desparsified_lasso</span></code></a>(X, y[, dof_ajdustement, ...])</p></td>
 <td><p>Desparsified Lasso with confidence intervals</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/hidimstat.ensemble_clustered_inference.html#hidimstat.ensemble_clustered_inference" title="hidimstat.ensemble_clustered_inference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble_clustered_inference</span></code></a>(X_init, y, ...)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.ensemble_clustered_inference.html#hidimstat.ensemble_clustered_inference" title="hidimstat.ensemble_clustered_inference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble_clustered_inference</span></code></a>(X_init, y, ...)</p></td>
 <td><p>Ensemble clustered inference algorithm</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.group_reid.html#hidimstat.group_reid" title="hidimstat.group_reid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">group_reid</span></code></a>(X, Y[, fit_Y, stationary, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/hidimstat.group_reid.html#hidimstat.group_reid" title="hidimstat.group_reid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">group_reid</span></code></a>(X, Y[, fit_Y, stationary, ...])</p></td>
 <td><p>Estimation of the covariance matrix using group Reid procedure</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/hidimstat.hd_inference.html#hidimstat.hd_inference" title="hidimstat.hd_inference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hd_inference</span></code></a>(X, y, method[, n_jobs, memory, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.hd_inference.html#hidimstat.hd_inference" title="hidimstat.hd_inference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hd_inference</span></code></a>(X, y, method[, n_jobs, memory, ...])</p></td>
 <td><p>Wrap-up high-dimensional inference procedures</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.multivariate_1D_simulation.html#hidimstat.multivariate_1D_simulation" title="hidimstat.multivariate_1D_simulation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multivariate_1D_simulation</span></code></a>([n_samples, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/hidimstat.multivariate_1D_simulation.html#hidimstat.multivariate_1D_simulation" title="hidimstat.multivariate_1D_simulation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multivariate_1D_simulation</span></code></a>([n_samples, ...])</p></td>
 <td><p>Generate 1D data with Toeplitz design matrix</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/hidimstat.permutation_test_cv.html#hidimstat.permutation_test_cv" title="hidimstat.permutation_test_cv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permutation_test_cv</span></code></a>(X, y[, n_permutations, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.permutation_test_cv.html#hidimstat.permutation_test_cv" title="hidimstat.permutation_test_cv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permutation_test_cv</span></code></a>(X, y[, n_permutations, ...])</p></td>
 <td><p>Cross-validated permutation test shuffling the target</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.reid.html#hidimstat.reid" title="hidimstat.reid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reid</span></code></a>(X, y[, eps, tol, max_iter, n_jobs, seed])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/hidimstat.reid.html#hidimstat.reid" title="hidimstat.reid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reid</span></code></a>(X, y[, eps, tol, max_iter, n_jobs, seed])</p></td>
 <td><p>Estimation of noise standard deviation using Reid procedure</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/hidimstat.standardized_svr.html#hidimstat.standardized_svr" title="hidimstat.standardized_svr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">standardized_svr</span></code></a>(X, y[, Cs, n_jobs])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.standardized_svr.html#hidimstat.standardized_svr" title="hidimstat.standardized_svr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">standardized_svr</span></code></a>(X, y[, Cs, n_jobs])</p></td>
 <td><p>Cross-validated SVR</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/hidimstat.zscore_from_pval.html#hidimstat.zscore_from_pval" title="hidimstat.zscore_from_pval"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zscore_from_pval</span></code></a>(pval[, one_minus_pval, distrib])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/hidimstat.zscore_from_pval.html#hidimstat.zscore_from_pval" title="hidimstat.zscore_from_pval"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zscore_from_pval</span></code></a>(pval[, one_minus_pval, distrib])</p></td>
 <td><p>Computing z-scores from one-sided p-values.</p></td>
 </tr>
 </tbody>

docs/auto_examples/plot_2D_simulation_example.html

@@ -473,7 +473,7 @@ <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 8.829 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 9.270 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">

docs/auto_examples/plot_diabetesFeatures_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_diabetesFeatures_importance_example_001.png" srcset="../_images/sphx_glr_plot_diabetesFeatures_importance_example_001.png" alt="plot diabetesFeatures importance example" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 46.623 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  29 MB</p>
+<img src="../_images/sphx_glr_plot_diabetesFeatures_importance_example_001.png" srcset="../_images/sphx_glr_plot_diabetesFeatures_importance_example_001.png" alt="plot diabetesFeatures importance example" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 47.285 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  32 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-diabetesfeatures-importance-example-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/6352d1acfa60f2083a9b7a62b5203e91/plot_diabetesFeatures_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_diabetesFeatures_importance_example.ipynb</span></code></a></p>

docs/auto_examples/plot_fmri_data_example.html

@@ -252,8 +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 99262464 of 291168628 bytes (34.1%,    1.9s remaining)
-Downloaded 216768512 of 291168628 bytes (74.4%,    0.7s remaining) ...done. (3 seconds, 0 min)
+Downloaded 107290624 of 291168628 bytes (36.8%,    1.7s remaining)
+Downloaded 238895104 of 291168628 bytes (82.0%,    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>
@@ -339,7 +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:   32.3s finished
+[Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   32.6s finished
 </pre></div>
 </div>
 </section>
@@ -458,8 +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 24.514 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  2679 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 25.149 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  2772 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>03:19.966</strong> total execution time for 3 files <strong>from auto_examples</strong>:</p>
+<p><strong>03:21.704</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>01:24.514</p></td>
-<td><p>2678.7</p></td>
+<td><p>01:25.149</p></td>
+<td><p>2772.0</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>01:08.829</p></td>
-<td><p>101.3</p></td>
+<td><p>01:09.270</p></td>
+<td><p>100.8</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_diabetesFeatures_importance_example.html#sphx-glr-auto-examples-plot-diabetesfeatures-importance-example-py"><span class="std std-ref">Variable Importance on diabetes dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_diabetesFeatures_importance_example.py</span></code>)</p></td>
-<td><p>00:46.623</p></td>
-<td><p>28.8</p></td>
+<td><p>00:47.285</p></td>
+<td><p>31.5</p></td>
 </tr>
 </tbody>
 </table>