diff --git a/docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip index e741989..4dd9305 100644 Binary files a/docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip and b/docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip differ diff --git a/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip index e245e40..1dd7176 100644 Binary files a/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip and b/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip differ diff --git a/docs/_images/sphx_glr_plot_fmri_data_example_001.png b/docs/_images/sphx_glr_plot_fmri_data_example_001.png index 92ce6ea..addb960 100644 Binary files a/docs/_images/sphx_glr_plot_fmri_data_example_001.png and b/docs/_images/sphx_glr_plot_fmri_data_example_001.png differ diff --git a/docs/_images/sphx_glr_plot_fmri_data_example_thumb.png b/docs/_images/sphx_glr_plot_fmri_data_example_thumb.png index 822c0cd..6d03408 100644 Binary files a/docs/_images/sphx_glr_plot_fmri_data_example_thumb.png and b/docs/_images/sphx_glr_plot_fmri_data_example_thumb.png differ diff --git a/docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt b/docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt index cfaabbe..f71152c 100644 --- a/docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt +++ b/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: diff --git a/docs/_sources/auto_examples/plot_diabetes_variable_importance_example.rst.txt b/docs/_sources/auto_examples/plot_diabetes_variable_importance_example.rst.txt index 2e3a61c..66f3dab 100644 --- a/docs/_sources/auto_examples/plot_diabetes_variable_importance_example.rst.txt +++ b/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: diff --git a/docs/_sources/auto_examples/plot_fmri_data_example.rst.txt b/docs/_sources/auto_examples/plot_fmri_data_example.rst.txt index 729f319..fb5e443 100644 --- a/docs/_sources/auto_examples/plot_fmri_data_example.rst.txt +++ b/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: diff --git a/docs/_sources/auto_examples/sg_execution_times.rst.txt b/docs/_sources/auto_examples/sg_execution_times.rst.txt index 1d4dd20..914a09f 100644 --- a/docs/_sources/auto_examples/sg_execution_times.rst.txt +++ b/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 diff --git a/docs/_sources/sg_execution_times.rst.txt b/docs/_sources/sg_execution_times.rst.txt index 2d28719..cb6943d 100644 --- a/docs/_sources/sg_execution_times.rst.txt +++ b/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 diff --git a/docs/api.html b/docs/api.html index a80f874..7c4cbac 100644 --- a/docs/api.html +++ b/docs/api.html @@ -138,7 +138,7 @@

Functions

BlockBasedImportance([estimator, ...])

-

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 :footcite:t:`Chamma_NeurIPS2023` and for group-level see :footcite:t:`Chamma_AAAI2024`. 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 "Mod_RF" 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 > 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.

+

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 et al.[#Chamma_NeurIPS2023]_ and for group-level see Chamma et al.[#Chamma_AAAI2024]_. 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 "Mod_RF" 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 > 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.

clustered_inference(X_init, y, ward, n_clusters)

Clustered inference algorithm

diff --git a/docs/auto_examples/plot_2D_simulation_example.html b/docs/auto_examples/plot_2D_simulation_example.html index bd5b83d..cfd7cda 100644 --- a/docs/auto_examples/plot_2D_simulation_example.html +++ b/docs/auto_examples/plot_2D_simulation_example.html @@ -473,8 +473,8 @@

Analysis of the results -

Total running time of the script: (1 minutes 0.085 seconds)

-

Estimated memory usage: 99 MB

+

Total running time of the script: (1 minutes 5.057 seconds)

+

Estimated memory usage: 101 MB

 -plot diabetes variable importance example

Total running time of the script: (0 minutes 46.171 seconds)

-

Estimated memory usage: 28 MB

+plot diabetes variable importance example

Total running time of the script: (0 minutes 47.274 seconds)

+

Estimated memory usage: 29 MB

-
[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
 

 

 
Now, let us run the algorithm introduced by Gaonkar et al. (c.f. References).
@@ -344,7 +339,7 @@ 
Making the inference with several algorithms
[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
@@ -463,8 +458,8 @@

Analysis of the results
show()

-

Total running time of the script: (1 minutes 26.859 seconds)

-

Estimated memory usage: 2762 MB

+

Total running time of the script: (1 minutes 26.070 seconds)

+

Estimated memory usage: 2790 MB