[WIP] update doscstrings NCL

doc/under_sampling.rst

@@ -347,10 +347,29 @@ place. The class can be used as::
 Our implementation offer to set the number of seeds to put in the set :math:`C`
 originally by setting the parameter ``n_seeds_S``.
 
-:class:`NeighbourhoodCleaningRule` will focus on cleaning the data than
-condensing them :cite:`laurikkala2001improving`. Therefore, it will used the
-union of samples to be rejected between the :class:`EditedNearestNeighbours`
-and the output a 3 nearest neighbors classifier. The class can be used as::
+:class:`NeighbourhoodCleaningRule` focuses more on cleaning the data than on
+reducing the number of samples :cite:`laurikkala2001improving`. It expands
+the :class:`EditedNearestNeighbours` in that, it further eliminates samples from
+the majority class, if they belong to the 3 closest neighbours of a sample from
+the majority class, where the majority, or all of the neighbours disagree with the
+minority. The procedure for the :class:`NeighbourhoodCleaningRule` is as follows:
+
+1. Split dataset into the class of interest C (minority) and the rest of the data O.
+2. Identify noisy data A1 in O, with edited nearest neighbor rule.
+3. For each (majority) class in O, if its observations are one of the 3 closest
+neighbors of a minority sample where all or most of those neighbors are not minority,
+add the observation to group A2.
+4. Reduce the original data S = T - ( A1 union A2 )
+
+The first step is an ENN, where most of neighbours need to disagree to remove a sample
+from the majority. The second step is a cleaning step, that further removes samples
+from the majority classes. To carry on the cleaning step there is one condition:
+it will only clean samples from classes that contain a minimum number of observations.
+The minimum number is regulated by the `threshold_cleaning` parameter. In the original
+article :cite:`laurikkala2001improving` samples would be removed if the class had at
+least half as many observations as those in the minority class.
+
+The class can be used as::
 
   >>> from imblearn.under_sampling import NeighbourhoodCleaningRule
   >>> ncr = NeighbourhoodCleaningRule()

imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py

@@ -28,7 +28,7 @@
 class NeighbourhoodCleaningRule(BaseCleaningSampler):
     """Undersample based on the neighbourhood cleaning rule.
 
-    This class uses ENN and a k-NN to remove noisy samples from the datasets.
+    This class uses ENN and a k-NN to remove noisy samples from the dataset.
 
     Read more in the :ref:`User Guide <condensed_nearest_neighbors>`.
 
@@ -40,19 +40,24 @@ class NeighbourhoodCleaningRule(BaseCleaningSampler):
         If ``int``, size of the neighbourhood to consider to compute the
         nearest neighbors. If object, an estimator that inherits from
         :class:`~sklearn.neighbors.base.KNeighborsMixin` that will be used to
-        find the nearest-neighbors. By default, it will be a 3-NN.
+        find the nearest-neighbors. By default, it explores the 3 closest
+        neighbors.
 
     kind_sel : {{"all", "mode"}}, default='all'
         Strategy to use in order to exclude samples in the ENN sampling.
 
-        - If ``'all'``, all neighbours will have to agree with the samples of
-          interest to not be excluded.
-        - If ``'mode'``, the majority vote of the neighbours will be used in
-          order to exclude a sample.
+        - If ``'all'``, all neighbours will have to agree with a sample in order
+          not to be excluded.
+        - If ``'mode'``, the majority of the neighbours will have to agree with
+         a sample in order not to be excluded.
 
         The strategy `"all"` will be less conservative than `'mode'`. Thus,
-        more samples will be removed when `kind_sel="all"` generally.
+        more samples will be removed when `kind_sel="all"`, generally.
 
+        Note that this parameter only applies to the cleaning step of the NCL.
+        The ENN is done using majority vote, as described in the original article.
+
+    #TODO: this is not the originally described threshold, fix
     threshold_cleaning : float, default=0.5
         Threshold used to whether consider a class or not during the cleaning
         after applying ENN. A class will be considered during cleaning when: