Fix imports sort + remove setup + add pyproject dependencies

.github/workflows/build_docs.yml

@@ -24,7 +24,7 @@ jobs:
       - uses: actions/setup-python@v5
       - name: Install dependencies
         run: |
-          pip install -r doc_conf/doc-requirements.txt
+          python -m pip install .[doc, plotting]
           pip install -e .
       - name: Sphinx build
         id: build-docs

.github/workflows/build_package.yml

@@ -27,13 +27,10 @@ jobs:
     - 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]
+        python -m pip install .[dependencies]
     - 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 +51,19 @@
        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 +51,19 @@
        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

@@ -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})
 

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
 

hidimstat/BBI.py

@@ -9,24 +9,17 @@
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
 from sklearn.linear_model import RidgeCV
-from sklearn.metrics import (
-    log_loss,
-    mean_absolute_error,
-    mean_squared_error,
-    roc_auc_score,
-    r2_score,
-)
-from sklearn.model_selection import KFold, GroupKFold
+from sklearn.metrics import (log_loss, mean_absolute_error, mean_squared_error,
+                             r2_score, roc_auc_score)
+from sklearn.model_selection import GroupKFold, KFold
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import OneHotEncoder, StandardScaler
 from sklearn.utils.validation import check_is_fitted
 
-from .compute_importance import (
-    joblib_compute_conditional,
-    joblib_compute_permutation,
-)
+from .compute_importance import (joblib_compute_conditional,
+                                 joblib_compute_permutation)
 from .Dnn_learner import Dnn_learner
-from .utils import convert_predict_proba, create_X_y, compute_imp_std
+from .utils import compute_imp_std, convert_predict_proba, create_X_y
 
 
 class BlockBasedImportance(BaseEstimator, TransformerMixin):

hidimstat/Dnn_learner.py

@@ -1,5 +1,6 @@
 import numpy as np
 from sklearn.base import BaseEstimator
+
 from .Dnn_learner_single import Dnn_learner_single
 
 

hidimstat/Dnn_learner_single.py

@@ -8,15 +8,8 @@
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.utils.validation import check_is_fitted
 
-from .utils import (
-    create_X_y,
-    dnn_net,
-    joblib_ensemble_dnnet,
-    ordinal_encode,
-    relu,
-    sigmoid,
-    softmax,
-)
+from .utils import (create_X_y, dnn_net, joblib_ensemble_dnnet, ordinal_encode,
+                    relu, sigmoid, softmax)
 
 
 class Dnn_learner_single(BaseEstimator):

hidimstat/__init__.py

@@ -5,8 +5,8 @@
 from .Dnn_learner_single import Dnn_learner_single
 from .ensemble_clustered_inference import ensemble_clustered_inference
 from .importance_functions import compute_loco
-from .knockoffs import model_x_knockoff
 from .knockoff_aggregation import knockoff_aggregation
+from .knockoffs import model_x_knockoff
 from .multi_sample_split import aggregate_quantiles
 from .noise_std import group_reid, reid
 from .permutation_test import permutation_test_cv

hidimstat/clustered_inference.py

@@ -1,10 +1,10 @@
 import numpy as np
-from sklearn.utils import resample
 from sklearn.preprocessing import StandardScaler
+from sklearn.utils import resample
 from sklearn.utils.validation import check_memory
 
+from .desparsified_lasso import desparsified_group_lasso, desparsified_lasso
 from .stat_tools import pval_from_cb
-from .desparsified_lasso import desparsified_lasso, desparsified_group_lasso
 
 
 def _subsampling(n_samples, train_size, groups=None, seed=0):

hidimstat/compute_importance.py

@@ -1,15 +1,14 @@
+import itertools
 import warnings
 from collections import Counter
-import itertools
+
 import numpy as np
 from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
 from sklearn.metrics import mean_absolute_error, r2_score, roc_auc_score
 from sklearn.model_selection import GridSearchCV
 
-from .RandomForestModified import (
-    RandomForestClassifierModified,
-    RandomForestRegressorModified,
-)
+from .RandomForestModified import (RandomForestClassifierModified,
+                                   RandomForestRegressorModified)
 from .utils import convert_predict_proba, ordinal_encode, sample_predictions
 
 warnings.filterwarnings("ignore")

hidimstat/desparsified_lasso.py

@@ -1,12 +1,12 @@
 import numpy as np
+from joblib import Parallel, delayed
 from numpy.linalg import multi_dot
 from scipy import stats
 from scipy.linalg import inv
-from joblib import Parallel, delayed
-from sklearn.utils.validation import check_memory
 from sklearn.linear_model import Lasso
+from sklearn.utils.validation import check_memory
 
-from .noise_std import reid, group_reid
+from .noise_std import group_reid, reid
 from .stat_tools import pval_from_two_sided_pval_and_sign
 
 

hidimstat/ensemble_clustered_inference.py

@@ -1,8 +1,8 @@
 import numpy as np
 from joblib import Parallel, delayed
 
-from .multi_sample_split import aggregate_medians, aggregate_quantiles
 from .clustered_inference import clustered_inference
+from .multi_sample_split import aggregate_medians, aggregate_quantiles
 
 
 def _ensembling(

hidimstat/gaussian_knockoff.py

@@ -5,7 +5,8 @@
 import warnings
 
 import numpy as np
-from sklearn.covariance import GraphicalLassoCV, empirical_covariance, ledoit_wolf
+from sklearn.covariance import (GraphicalLassoCV, empirical_covariance,
+                                ledoit_wolf)
 from sklearn.utils.validation import check_memory
 
 

hidimstat/importance_functions.py

@@ -1,10 +1,11 @@
-from hidimstat.Dnn_learner_single import Dnn_learner_single
 import numpy as np
 from scipy.stats import ttest_1samp
 from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
 from sklearn.model_selection import GridSearchCV
 from sklearn.preprocessing import OneHotEncoder
 
+from hidimstat.Dnn_learner_single import Dnn_learner_single
+
 
 def compute_loco(X, y, ntree=100, problem_type="regression", use_dnn=True, seed=2024):
     """

hidimstat/knockoff_aggregation.py

@@ -6,7 +6,8 @@
 from sklearn.utils import check_random_state
 from sklearn.utils.validation import check_memory
 
-from .gaussian_knockoff import _estimate_distribution, gaussian_knockoff_generation
+from .gaussian_knockoff import (_estimate_distribution,
+                                gaussian_knockoff_generation)
 from .stat_coef_diff import stat_coef_diff
 from .utils import fdr_threshold, quantile_aggregation