Permutation_test (1/4): add comments and docstring of the functions (#…

…111) * Fix gitignore * Remove not necesaru function * refactor files * Add option to pytest for detection of test * Fix test * Remove not necessary normalisation * Comment step_down_maxt algo * Modify function of the integrate the difference. * format stat tools * add formating * Fixe bug * Format files * Fix a bug from the reformating * Better function for empty matrix * Add a citation * Fix format * Improve actual tests * Add two tests * format type * change default values * Format file * Change default argument for parallelization * Update example * include all * Modification folowwing suggestion * Format file * Small modification * Add comment on the non usage of CV * Format file
mind-inria · Feb 18, 2025 · 349ade8 · 349ade8
1 parent 16ce023
commit 349ade8
Show file tree

Hide file tree

Showing 8 changed files with 304 additions and 178 deletions.
diff --git a/doc_conf/api.rst b/doc_conf/api.rst
@@ -26,7 +26,8 @@ Functions
    knockoff_aggregation
    model_x_knockoff
    multivariate_1D_simulation
-   permutation_test_cv
+   permutation_test
+   permutation_test_pval
    reid
    standardized_svr
    zscore_from_pval

diff --git a/doc_conf/references.bib b/doc_conf/references.bib
@@ -241,6 +241,24 @@ @article{meinshausen2009p
   publisher={Taylor \& Francis}
 }
 
+@book{westfall1993resampling,
+  title={Resampling-based multiple testing: Examples and methods for p-value adjustment},
+  author={Westfall, Peter H and Young, S Stanley},
+  volume={279},
+  year={1993},
+  publisher={John Wiley \& Sons}
+}
+
+@article{hirschhorn2005genome,
+  title={Genome-wide association studies for common diseases and complex traits},
+  author={Hirschhorn, Joel N and Daly, Mark J},
+  journal={Nature reviews genetics},
+  volume={6},
+  number={2},
+  pages={95--108},
+  year={2005},
+  publisher={Nature Publishing Group UK London}
+}
 @article{gaonkar_deriving_2012,
 	title = {Deriving statistical significance maps for {SVM} based image classification and group comparisons},
 	volume = {15},

diff --git a/examples/plot_fmri_data_example.py b/examples/plot_fmri_data_example.py
@@ -52,15 +52,18 @@
 from sklearn.cluster import FeatureAgglomeration
 from sklearn.feature_extraction import image
 from sklearn.linear_model import Ridge
+from sklearn.svm import LinearSVR
 from sklearn.utils import Bunch
 
 from hidimstat.ada_svr 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.permutation_test import permutation_test, permutation_test_pval
 from hidimstat.standardized_svr import standardized_svr
 from hidimstat.stat_tools import pval_from_scale, zscore_from_pval
 
+n_job = None
+
 
 #############################################################################
 # Function to fetch and preprocess Haxby dataset
@@ -151,19 +154,28 @@ def preprocess_haxby(subject=2, memory=None):
 
 SVR_permutation_test_inference = False
 if SVR_permutation_test_inference:
-    # We computed the regularization parameter by CV (C = 0.1)
-    pval_corr_svr_perm_test, one_minus_pval_corr_svr_perm_test = permutation_test_cv(
-        X, y, n_permutations=50, C=0.1
+    # It will be better to associate cross validation with the estimator
+    # but for a sake of time, this is not done.
+    estimator = LinearSVR()
+    weight_svr, weight_svr_distribution = permutation_test(
+        X, y, estimator, n_permutations=50
+    )
+    pval_corr_svr_perm_test, one_minus_pval_corr_svr_perm_test = permutation_test_pval(
+        weight_svr, weight_svr_distribution
     )
 
 # Another method is to compute the p-values by permutation test from the
 # Ridge decoder. The solution provided by this method should be very close to
 # the previous one and the computation time is much shorter: around 20 seconds.
-
+# We computed the parameter from a cross valisation (alpha = 0.0215)
+# It will be better to use RidgeCV but for a sake of time, this is not done.
 estimator = Ridge()
-pval_corr_ridge_perm_test, one_minus_pval_corr_ridge_perm_test = permutation_test(
+weight_ridge, weight_ridge_distribution = permutation_test(
     X, y, estimator=estimator, n_permutations=200
 )
+pval_corr_ridge_perm_test, one_minus_pval_corr_ridge_perm_test = permutation_test_pval(
+    weight_ridge, weight_ridge_distribution
+)
 
 #############################################################################
 # Now, let us run the algorithm introduced by Gaonkar et al. (c.f. References).
@@ -305,4 +317,4 @@ def plot_map(
 # (EnCluDL) seems realistic as we recover the visual cortex and do not make
 # spurious discoveries.
 
-show()
+# show()
diff --git a/pyproject.toml b/pyproject.toml
@@ -80,4 +80,8 @@ where = ["src"]
 
 
 [tool.hatch.version]
-source = "vcs"
+source = "vcs"
+
+#pyproject.toml
+[tool.pytest.ini_options]
+addopts = "--ignore=src"   # ignore src directory
diff --git a/src/hidimstat/__init__.py b/src/hidimstat/__init__.py
@@ -7,7 +7,7 @@
 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
+from .permutation_test import permutation_test, permutation_test_pval
 from .scenario import multivariate_1D_simulation
 from .standardized_svr import standardized_svr
 from .stat_tools import zscore_from_pval
@@ -34,7 +34,8 @@
     "knockoff_aggregation",
     "model_x_knockoff",
     "multivariate_1D_simulation",
-    "permutation_test_cv",
+    "permutation_test",
+    "permutation_test_pval",
     "reid",
     "standardized_svr",
     "zscore_from_pval",