docstring fix

hcga/analysis.py

@@ -653,18 +653,18 @@ def classify_pairwise(  # pylint: disable=too-many-locals
 
     The top features for each pair with high enough accuracies are collected in a list,
     for later analysis.
+
     Args:
         features (dataframe): extracted features
         features_info (dataframe): features information
         model (str): model to preform analysis
         graph_removal (float): remove samples with more than graph_removal % bad values
         n_top_features (int): number of top features to save
         reduce_set (bool): is True, the classification will be rerun
-                           on a reduced set of top features (from shapely analysis)
+            on a reduced set of top features (from shapely analysis)
         reduce_set_size (int): number of features to keep for reduces set
         reduced_set_max_correlation (float): to discared highly correlated top features
-                                             in reduced set of features
-
+            in reduced set of features
         n_repeats (int): number of k-fold repeats
         n_splits (int): numbere of split for k-fold, None=automatic estimation
         analysis_type (str): 'classification' or 'regression'

hcga/features/centralities_basic.py

@@ -93,9 +93,9 @@ class CentralitiesBasic(FeatureClass):
 
     Here we implement:
     Degree Centrality
-    Eigenvector Centrality [1]_[2]_
-    Closeness Centrality [3]_[4]_
-    Betweenness Centrality [5]_[6]_[7]_[8]_
+    Eigenvector Centrality [1]_ [2]_
+    Closeness Centrality [3]_ [4]_
+    Betweenness Centrality [5]_ [6]_ [7]_ [8]_
     Harmonic Centrality [9]_
 
     References

hcga/features/clustering.py

@@ -33,7 +33,7 @@ class Clustering(FeatureClass):
     We compute:
     The number of triangles
     Transitivity [1]_
-    Clustering [2]_[3]_[4]_
+    Clustering [2]_ [3]_ [4]_
 
     References
     ----------