Fix format

doc_conf/conf.py

@@ -259,8 +259,8 @@
     "abort_on_example_error": False,
     "image_scrapers": scrapers,
     "show_memory": True,
-    'filename_pattern': r'\.py',
-    'ignore_pattern': r'__init__\.py',
+    "filename_pattern": r"\.py",
+    "ignore_pattern": r"__init__\.py",
     # 'reference_url': {
     #     'numpy': 'http://docs.scipy.org/doc/numpy-1.9.1',
     #     'scipy': 'http://docs.scipy.org/doc/scipy-0.17.0/reference',

examples/inference_model/ada_svr.py

@@ -155,27 +155,27 @@
 #
 # - The distribution of the coefficients of SVR is normal centred around zeros.
 # - The method is valid for large sample sizes.
-# - The method has the linear models assumptions: linearity, normality, 
+# - The method has the linear models assumptions: linearity, normality,
 #   homoscedasticity, independence, fixed features, absence of multicollinearity
 #   (see the book of :footcite:ct:`Molnar 2012<molnar2020interpretable>`
 #   for details)
 #
 # **Advantages**:
-# 
+#
 # - The method is fast because it uses the central limit theorem to estimate
 #   the standard deviation of the distribution of the coefficients of SVR.
-# - The method has the advantage of linear models: transparency of 
-#   the prediction, high level of collective experiance and expertise 
-#   and a guarantee of convergence. (see the book of 
+# - The method has the advantage of linear models: transparency of
+#   the prediction, high level of collective experiance and expertise
+#   and a guarantee of convergence. (see the book of
 #   :footcite:ct:`Molnar 2012<molnar2020interpretable>` for details)
 #
 # **Disadvantages**:
 #
 # - The method assumes that the distribution of the coefficients of SVR is normal centred around zeros.
 # - The method is not valid for small sample sizes.
-# - The method has all the disadvantages of linear models: only for linear 
-#   relationships, not good predicting performance, unintuitive. 
-#   (see the book of 
+# - The method has all the disadvantages of linear models: only for linear
+#   relationships, not good predicting performance, unintuitive.
+#   (see the book of
 #   :footcite:ct:`Molnar 2012<molnar2020interpretable>` for details)
 #
 # **Conclusion**:

src/hidimstat/ada_svr.py

@@ -1,6 +1,8 @@
 import numpy as np
 from hidimstat.stat_tools import pval_from_scale
 
+__all__ = ["ada_svr", "ada_svr_pvalue"]
+
 
 def ada_svr(X, y, rcond=1e-3):
     """

src/hidimstat/visualisation/plot_dataset.py

@@ -7,7 +7,7 @@
 def plot_dataset1D(X, y, beta, title="Toy dataset"):
     """
     Plot a 1D toy dataset with the true regression line.
-    
+
     Parameters
     ----------
     X : ndarray, shape (n_samples, n_features)
@@ -58,7 +58,7 @@ def plot_dataset1D(X, y, beta, title="Toy dataset"):
 def plot_validate_variable_importance(beta, beta_hat, vmin=0.0, vmax=1.0):
     """
     Plot for validating of the variable importance estimation.
-    
+
     Parameters
     ----------
     beta : ndarray, shape (n_features,)
@@ -184,7 +184,7 @@ def plot_pvalue_H1(
 ):
     """
     Plot for the confidence in the hypotheses that the variables are not important.
-    
+
     Parameters
     ----------
     beta_hat : ndarray, shape (n_features,)