Merge remote-tracking branch 'refs/remotes/origin/main'

docs/_downloads/205fc01252fa85326bc85c58c2e96a08/plot_diabetesFeatures_importance_example.py → docs/_downloads/6f624092537330c9f373c01828b2b9ae/plot_diabetes_variable_importance_example.py

@@ -35,11 +35,11 @@
 bbi_perm = BlockBasedImportance(
     estimator="RF",
     importance_estimator="Mod_RF",
-    do_hyper=True,
-    dict_hyper=None,
+    do_hypertuning=True,
+    dict_hypertuning=None,
     conditional=False,
     group_stacking=False,
-    prob_type="regression",
+    problem_type="regression",
     k_fold=k_fold,
     list_nominal=list_nominal,
     n_jobs=10,
@@ -58,11 +58,11 @@
 bbi_cond = BlockBasedImportance(
     estimator="RF",
     importance_estimator="Mod_RF",
-    do_hyper=True,
-    dict_hyper=None,
+    do_hypertuning=True,
+    dict_hypertuning=None,
     conditional=True,
     group_stacking=False,
-    prob_type="regression",
+    problem_type="regression",
     k_fold=k_fold,
     list_nominal=list_nominal,
     n_jobs=10,

docs/_downloads/6352d1acfa60f2083a9b7a62b5203e91/plot_diabetesFeatures_importance_example.ipynb → docs/_downloads/a70e28075a283d5e3fe675ced733c459/plot_diabetes_variable_importance_example.ipynb

@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "bbi_perm = BlockBasedImportance(\n    estimator=\"RF\",\n    importance_estimator=\"Mod_RF\",\n    do_hyper=True,\n    dict_hyper=None,\n    conditional=False,\n    group_stacking=False,\n    prob_type=\"regression\",\n    k_fold=k_fold,\n    list_nominal=list_nominal,\n    n_jobs=10,\n    verbose=0,\n    n_perm=100,\n)\nbbi_perm.fit(X, y)\nprint(\"Computing the importance scores with standard permutation\")\nresults_perm = bbi_perm.compute_importance()\npvals_perm = -np.log10(results_perm[\"pval\"] + 1e-10)"
+        "bbi_perm = BlockBasedImportance(\n    estimator=\"RF\",\n    importance_estimator=\"Mod_RF\",\n    do_hypertuning=True,\n    dict_hypertuning=None,\n    conditional=False,\n    group_stacking=False,\n    problem_type=\"regression\",\n    k_fold=k_fold,\n    list_nominal=list_nominal,\n    n_jobs=10,\n    verbose=0,\n    n_perm=100,\n)\nbbi_perm.fit(X, y)\nprint(\"Computing the importance scores with standard permutation\")\nresults_perm = bbi_perm.compute_importance()\npvals_perm = -np.log10(results_perm[\"pval\"] + 1e-10)"
       ]
     },
     {
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "bbi_cond = BlockBasedImportance(\n    estimator=\"RF\",\n    importance_estimator=\"Mod_RF\",\n    do_hyper=True,\n    dict_hyper=None,\n    conditional=True,\n    group_stacking=False,\n    prob_type=\"regression\",\n    k_fold=k_fold,\n    list_nominal=list_nominal,\n    n_jobs=10,\n    verbose=0,\n    n_perm=100,\n)\nbbi_cond.fit(X, y)\nprint(\"Computing the importance scores with conditional permutation\")\nresults_cond = bbi_cond.compute_importance()\npvals_cond = -np.log10(results_cond[\"pval\"] + 1e-5)"
+        "bbi_cond = BlockBasedImportance(\n    estimator=\"RF\",\n    importance_estimator=\"Mod_RF\",\n    do_hypertuning=True,\n    dict_hypertuning=None,\n    conditional=True,\n    group_stacking=False,\n    problem_type=\"regression\",\n    k_fold=k_fold,\n    list_nominal=list_nominal,\n    n_jobs=10,\n    verbose=0,\n    n_perm=100,\n)\nbbi_cond.fit(X, y)\nprint(\"Computing the importance scores with conditional permutation\")\nresults_cond = bbi_cond.compute_importance()\npvals_cond = -np.log10(results_cond[\"pval\"] + 1e-5)"
       ]
     },
     {

docs/_sources/auto_examples/index.rst.txt

@@ -23,10 +23,10 @@ Examples Gallery
 
 .. only:: html
 
-  .. image:: /auto_examples/images/thumb/sphx_glr_plot_diabetesFeatures_importance_example_thumb.png
+  .. image:: /auto_examples/images/thumb/sphx_glr_plot_diabetes_variable_importance_example_thumb.png
     :alt:
 
-  :ref:`sphx_glr_auto_examples_plot_diabetesFeatures_importance_example.py`
+  :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py`
 
 .. raw:: html
 
@@ -78,7 +78,7 @@ Examples Gallery
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_diabetesFeatures_importance_example
+   /auto_examples/plot_diabetes_variable_importance_example
    /auto_examples/plot_2D_simulation_example
    /auto_examples/plot_fmri_data_example
 

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 9.270 seconds)
+   **Total running time of the script:** (1 minutes 0.085 seconds)
 
-**Estimated memory usage:**  101 MB
+**Estimated memory usage:**  99 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_2D_simulation_example.py:

docs/_sources/auto_examples/plot_diabetesFeatures_importance_example.rst.txt → docs/_sources/auto_examples/plot_diabetes_variable_importance_example.rst.txt

@@ -2,20 +2,20 @@
 .. DO NOT EDIT.
 .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
 .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "auto_examples/plot_diabetesFeatures_importance_example.py"
+.. "auto_examples/plot_diabetes_variable_importance_example.py"
 .. LINE NUMBERS ARE GIVEN BELOW.
 
 .. only:: html
 
     .. note::
         :class: sphx-glr-download-link-note
 
-        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_diabetesFeatures_importance_example.py>`
+        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_diabetes_variable_importance_example.py>`
         to download the full example code.
 
 .. rst-class:: sphx-glr-example-title
 
-.. _sphx_glr_auto_examples_plot_diabetesFeatures_importance_example.py:
+.. _sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py:
 
 
 Variable Importance on diabetes dataset
@@ -72,11 +72,11 @@ Standard Variable Importance
     bbi_perm = BlockBasedImportance(
         estimator="RF",
         importance_estimator="Mod_RF",
-        do_hyper=True,
-        dict_hyper=None,
+        do_hypertuning=True,
+        dict_hypertuning=None,
         conditional=False,
         group_stacking=False,
-        prob_type="regression",
+        problem_type="regression",
         k_fold=k_fold,
         list_nominal=list_nominal,
         n_jobs=10,
@@ -116,11 +116,11 @@ Conditional Variable Importance
     bbi_cond = BlockBasedImportance(
         estimator="RF",
         importance_estimator="Mod_RF",
-        do_hyper=True,
-        dict_hyper=None,
+        do_hypertuning=True,
+        dict_hypertuning=None,
         conditional=True,
         group_stacking=False,
-        prob_type="regression",
+        problem_type="regression",
         k_fold=k_fold,
         list_nominal=list_nominal,
         n_jobs=10,
@@ -182,9 +182,9 @@ Plotting the comparison
 
 
 
-.. image-sg:: /auto_examples/images/sphx_glr_plot_diabetesFeatures_importance_example_001.png
-   :alt: plot diabetesFeatures importance example
-   :srcset: /auto_examples/images/sphx_glr_plot_diabetesFeatures_importance_example_001.png
+.. image-sg:: /auto_examples/images/sphx_glr_plot_diabetes_variable_importance_example_001.png
+   :alt: plot diabetes variable importance example
+   :srcset: /auto_examples/images/sphx_glr_plot_diabetes_variable_importance_example_001.png
    :class: sphx-glr-single-img
 
 
@@ -194,24 +194,24 @@ Plotting the comparison
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 47.285 seconds)
+   **Total running time of the script:** (0 minutes 46.171 seconds)
 
-**Estimated memory usage:**  32 MB
+**Estimated memory usage:**  28 MB
 
 
-.. _sphx_glr_download_auto_examples_plot_diabetesFeatures_importance_example.py:
+.. _sphx_glr_download_auto_examples_plot_diabetes_variable_importance_example.py:
 
 .. only:: html
 
   .. container:: sphx-glr-footer sphx-glr-footer-example
 
     .. container:: sphx-glr-download sphx-glr-download-jupyter
 
-      :download:`Download Jupyter notebook: plot_diabetesFeatures_importance_example.ipynb <plot_diabetesFeatures_importance_example.ipynb>`
+      :download:`Download Jupyter notebook: plot_diabetes_variable_importance_example.ipynb <plot_diabetes_variable_importance_example.ipynb>`
 
     .. container:: sphx-glr-download sphx-glr-download-python
 
-      :download:`Download Python source code: plot_diabetesFeatures_importance_example.py <plot_diabetesFeatures_importance_example.py>`
+      :download:`Download Python source code: plot_diabetes_variable_importance_example.py <plot_diabetes_variable_importance_example.py>`
 
 
 .. only:: html

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 107290624 of 291168628 bytes (36.8%,    1.7s remaining)    Downloaded 238895104 of 291168628 bytes (82.0%,    0.4s remaining) ...done. (3 seconds, 0 min)
+    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)
     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:   32.6s finished
+    [Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   31.3s finished
 
 
 
@@ -599,9 +599,9 @@ spurious discoveries.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 25.149 seconds)
+   **Total running time of the script:** (1 minutes 26.859 seconds)
 
-**Estimated memory usage:**  2772 MB
+**Estimated memory usage:**  2762 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
 =================
-**03:21.704** total execution time for 3 files **from auto_examples**:
+**03:13.114** 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``)
-     - 01:25.149
-     - 2772.0
+     - 01:26.859
+     - 2762.1
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``plot_2D_simulation_example.py``)
-     - 01:09.270
-     - 100.8
-   * - :ref:`sphx_glr_auto_examples_plot_diabetesFeatures_importance_example.py` (``plot_diabetesFeatures_importance_example.py``)
-     - 00:47.285
-     - 31.5
+     - 01:00.085
+     - 98.6
+   * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``plot_diabetes_variable_importance_example.py``)
+     - 00:46.171
+     - 28.4

docs/_sources/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**03:21.704** total execution time for 3 files **from all galleries**:
+**03:13.114** 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``)
-     - 01:25.149
-     - 2772.0
+     - 01:26.859
+     - 2762.1
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``../examples/plot_2D_simulation_example.py``)
-     - 01:09.270
-     - 100.8
-   * - :ref:`sphx_glr_auto_examples_plot_diabetesFeatures_importance_example.py` (``../examples/plot_diabetesFeatures_importance_example.py``)
-     - 00:47.285
-     - 31.5
+     - 01:00.085
+     - 98.6
+   * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``../examples/plot_diabetes_variable_importance_example.py``)
+     - 00:46.171
+     - 28.4

docs/api.html

@@ -5,7 +5,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
-    <title>API Documentation &#8212; hidimstat 0.1.0 documentation</title>
+    <title>API Documentation &#8212; HiDimStat 0.1.0 documentation</title>
     <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
     <link rel="stylesheet" type="text/css" href="_static/bootstrap-sphinx.css?v=284a2d1d" />
     <link rel="stylesheet" type="text/css" href="_static/style.css?v=fbda0898" />
@@ -41,7 +41,7 @@
           <span class="icon-bar"></span>
         </button>
         <a class="navbar-brand" href="index.html">
-          hidimstat</a>
+          HiDimStat</a>
         <span class="navbar-text navbar-version pull-left"><b>0.1.0</b></span>
       </div>
 
@@ -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),</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>
 </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/index.html

@@ -5,7 +5,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
-    <title>Examples Gallery &#8212; hidimstat 0.1.0 documentation</title>
+    <title>Examples Gallery &#8212; HiDimStat 0.1.0 documentation</title>
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
     <link rel="stylesheet" type="text/css" href="../_static/bootstrap-sphinx.css?v=284a2d1d" />
     <link rel="stylesheet" type="text/css" href="../_static/style.css?v=fbda0898" />
@@ -39,7 +39,7 @@
           <span class="icon-bar"></span>
         </button>
         <a class="navbar-brand" href="../index.html">
-          hidimstat</a>
+          HiDimStat</a>
         <span class="navbar-text navbar-version pull-left"><b>0.1.0</b></span>
       </div>
 
@@ -110,8 +110,8 @@
 
   <section id="examples-gallery">
 <span id="general-examples"></span><h1>Examples Gallery<a class="headerlink" href="#examples-gallery" title="Link to this heading">¶</a></h1>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example compares the standard permutation approach for variable importance and its conditi..."><img alt="" src="../_images/sphx_glr_plot_diabetesFeatures_importance_example_thumb.png" />
-<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></p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example compares the standard permutation approach for variable importance and its conditi..."><img alt="" src="../_images/sphx_glr_plot_diabetes_variable_importance_example_thumb.png" />
+<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></p>
   <div class="sphx-glr-thumbnail-title">Variable Importance on diabetes dataset</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the advantages of spatially relaxed inference when dealing with high-dimensi..."><img alt="" src="../_images/sphx_glr_plot_2D_simulation_example_thumb.png" />
 <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></p>