Merge remote-tracking branch 'origin' into bbi_examples

.github/workflows/build_docs.yml

@@ -22,10 +22,12 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
+        with:
+            python-version: '3.12'
       - name: Install dependencies
         run: |
-          pip install -r doc_conf/doc-requirements.txt
-          pip install -e .
+          python -m pip install -e .
+          python -m pip install .[doc,plotting]
       - name: Sphinx build
         id: build-docs
         run: |

.github/workflows/build_package.yml

@@ -12,28 +12,22 @@ on:
 jobs:
   build:
 
-    runs-on: ubuntu-latest
+    runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
       matrix:
         python-version: ["3.12"]
+        os: [ubuntu-latest, macos-latest, windows-latest]
 
     steps:
     - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install flake8 pytest
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-    # - name: Lint with flake8
-    #  uses: py-actions/[email protected]
     - name: Generate coverage report
       run: |
-        pip install pytest pytest-cov
+        python -m pip install .[test]
         pytest --cov=./ --cov-report=xml
     - name: Upload coverage reports to Codecov
       uses: codecov/[email protected]

doc_conf/conf.py

@@ -13,12 +13,12 @@
 # serve to show the default.
 
 import os
-import sys
 import warnings
-import sphinx_gallery
-import sphinx_bootstrap_theme
 from distutils.version import LooseVersion
+
 import matplotlib
+import sphinx_bootstrap_theme
+import sphinx_gallery
 
 # Disable agg warnings in doc
 warnings.filterwarnings(

docs/_downloads/0f0a1edc554c21f6f7fda8f53284928a/plot_2D_simulation_example.py

@@ -51,20 +51,20 @@
        arXiv preprint arXiv:2106.02590.
 """
 
+import matplotlib.pyplot as plt
+
 #############################################################################
 # Imports needed for this script
 # ------------------------------
 import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.feature_extraction import image
 from sklearn.cluster import FeatureAgglomeration
+from sklearn.feature_extraction import image
 
-from hidimstat.scenario import multivariate_simulation
-from hidimstat.stat_tools import zscore_from_pval, pval_from_cb
-from hidimstat.desparsified_lasso import desparsified_lasso
 from hidimstat.clustered_inference import clustered_inference
+from hidimstat.desparsified_lasso import desparsified_lasso
 from hidimstat.ensemble_clustered_inference import ensemble_clustered_inference
-
+from hidimstat.scenario import multivariate_simulation
+from hidimstat.stat_tools import pval_from_cb, zscore_from_pval
 
 #############################################################################
 # Specific plotting functions

docs/_downloads/6f624092537330c9f373c01828b2b9ae/plot_diabetes_variable_importance_example.py

@@ -10,10 +10,11 @@
 # Imports needed for this script
 # ------------------------------
 
+import matplotlib.pyplot as plt
 import numpy as np
-from hidimstat.BBI import BlockBasedImportance
 from sklearn.datasets import load_diabetes
-import matplotlib.pyplot as plt
+
+from hidimstat.BBI import BlockBasedImportance
 
 plt.rcParams.update({"font.size": 14})
 

docs/_downloads/76c0979bf6618aa210fd11bb28dcf896/plot_fmri_data_example.py

@@ -45,21 +45,21 @@
 # ------------------------------
 import numpy as np
 import pandas as pd
-from sklearn.utils import Bunch
-from sklearn.cluster import FeatureAgglomeration
-from sklearn.feature_extraction import image
-from sklearn.linear_model import Ridge
 from nilearn import datasets
-from nilearn.input_data import NiftiMasker
 from nilearn.image import mean_img
+from nilearn.input_data import NiftiMasker
 from nilearn.plotting import plot_stat_map, show
+from sklearn.cluster import FeatureAgglomeration
+from sklearn.feature_extraction import image
+from sklearn.linear_model import Ridge
+from sklearn.utils import Bunch
 
-from hidimstat.stat_tools import zscore_from_pval, pval_from_scale
-from hidimstat.standardized_svr import standardized_svr
-from hidimstat.permutation_test import permutation_test, permutation_test_cv
 from hidimstat.adaptive_permutation_threshold import ada_svr
 from hidimstat.clustered_inference import clustered_inference
 from hidimstat.ensemble_clustered_inference import ensemble_clustered_inference
+from hidimstat.permutation_test import permutation_test, permutation_test_cv
+from hidimstat.standardized_svr import standardized_svr
+from hidimstat.stat_tools import pval_from_scale, zscore_from_pval
 
 
 #############################################################################

examples/plot_2D_simulation_example.py

@@ -51,20 +51,20 @@
        arXiv preprint arXiv:2106.02590.
 """
 
+import matplotlib.pyplot as plt
+
 #############################################################################
 # Imports needed for this script
 # ------------------------------
 import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.feature_extraction import image
 from sklearn.cluster import FeatureAgglomeration
+from sklearn.feature_extraction import image
 
-from hidimstat.scenario import multivariate_simulation
-from hidimstat.stat_tools import zscore_from_pval, pval_from_cb
-from hidimstat.desparsified_lasso import desparsified_lasso
 from hidimstat.clustered_inference import clustered_inference
+from hidimstat.desparsified_lasso import desparsified_lasso
 from hidimstat.ensemble_clustered_inference import ensemble_clustered_inference
-
+from hidimstat.scenario import multivariate_simulation
+from hidimstat.stat_tools import pval_from_cb, zscore_from_pval
 
 #############################################################################
 # Specific plotting functions

examples/plot_diabetes_variable_importance_example.py

@@ -46,10 +46,11 @@
 # Imports needed for this script
 # ------------------------------
 
+import matplotlib.pyplot as plt
 import numpy as np
-from hidimstat.BBI import BlockBasedImportance
 from sklearn.datasets import load_diabetes
-import matplotlib.pyplot as plt
+
+from hidimstat.BBI import BlockBasedImportance
 
 plt.rcParams.update({"font.size": 14})
 

examples/plot_fmri_data_example.py

@@ -45,21 +45,21 @@
 # ------------------------------
 import numpy as np
 import pandas as pd
-from sklearn.utils import Bunch
-from sklearn.cluster import FeatureAgglomeration
-from sklearn.feature_extraction import image
-from sklearn.linear_model import Ridge
 from nilearn import datasets
-from nilearn.input_data import NiftiMasker
 from nilearn.image import mean_img
+from nilearn.input_data import NiftiMasker
 from nilearn.plotting import plot_stat_map, show
+from sklearn.cluster import FeatureAgglomeration
+from sklearn.feature_extraction import image
+from sklearn.linear_model import Ridge
+from sklearn.utils import Bunch
 
-from hidimstat.stat_tools import zscore_from_pval, pval_from_scale
-from hidimstat.standardized_svr import standardized_svr
-from hidimstat.permutation_test import permutation_test, permutation_test_cv
 from hidimstat.adaptive_permutation_threshold import ada_svr
 from hidimstat.clustered_inference import clustered_inference
 from hidimstat.ensemble_clustered_inference import ensemble_clustered_inference
+from hidimstat.permutation_test import permutation_test, permutation_test_cv
+from hidimstat.standardized_svr import standardized_svr
+from hidimstat.stat_tools import pval_from_scale, zscore_from_pval
 
 
 #############################################################################

examples_not_exhibited/plot_fig_1_nguyen_et_al.py

@@ -12,11 +12,12 @@
 """
 import matplotlib.pyplot as plt
 import numpy as np
+from joblib import Parallel, delayed
+from sklearn.preprocessing import StandardScaler
+
 from hidimstat.knockoffs import knockoff_aggregation, model_x_knockoff
 from hidimstat.knockoffs.data_simulation import simu_data
 from hidimstat.knockoffs.utils import cal_fdp_power
-from joblib import Parallel, delayed
-from sklearn.preprocessing import StandardScaler
 
 color_blue = "#1f77b4"
 color_teal = "#1fbecf"

examples_not_exhibited/plot_meg_data_example.py

@@ -23,14 +23,15 @@
 """
 
 import os
-import numpy as np
+
 import matplotlib.image as mpimg
 import matplotlib.pyplot as plt
 import mne
-from scipy.sparse.csgraph import connected_components
+import numpy as np
 from mne.datasets import sample, somato
-from mne.inverse_sparse.mxne_inverse import _prepare_gain, _make_sparse_stc
-from mne.minimum_norm import make_inverse_operator, apply_inverse
+from mne.inverse_sparse.mxne_inverse import _make_sparse_stc, _prepare_gain
+from mne.minimum_norm import apply_inverse, make_inverse_operator
+from scipy.sparse.csgraph import connected_components
 from sklearn.cluster import FeatureAgglomeration
 from sklearn.metrics.pairwise import pairwise_distances