DOC replace nominal by categorical when needed

Author: glemaitre

doc/metrics.rst

@@ -87,7 +87,7 @@ Value Difference Metric
 The class :class:`~imblearn.metrics.pairwise.ValueDifferenceMetric` is
 implementing the Value Difference Metric proposed in
 :cite:`stanfill1986toward`. This measure is used to compute the proximity
-of two samples composed of only nominal values.
+of two samples composed of only categorical values.
 
 Given a single feature, categories with similar correlation with the target
 vector will be considered closer. Let's give an example to illustrate this

doc/over_sampling.rst

@@ -212,9 +212,9 @@ columns are belonging to the same categories originally presented without any
 other extra interpolation.
 
 However, :class:`SMOTENC` is only working when data is a mixed of numerical and
-categorical features. If data are made of only nominal categorical data, one
-can use the :class:`SMOTEN` variant :cite:`chawla2002smote`. The algorithm
-changes in two ways:
+categorical features. If data are made of only categorical data, one can use
+the :class:`SMOTEN` variant :cite:`chawla2002smote`. The algorithm changes in
+two ways:
 
 * the nearest neighbors search does not rely on the Euclidean distance. Indeed,
   the value difference metric (VDM) also implemented in the class

doc/whats_new/v0.8.rst

@@ -16,11 +16,12 @@ New features
   :pr:`780` by :user:`Aurélien Massiot <AurelienMassiot>`.
 
 - Add the class :class:`imblearn.metrics.pairwise.ValueDifferenceMetric` to
-  compute pairwise distances between samples containing only nominal values.
+  compute pairwise distances between samples containing only categorical
+  values.
   :pr:`796` by :user:`Guillaume Lemaitre <glemaitre>`.
 
 - Add the class :class:`imblearn.over_sampling.SMOTEN` to over-sample data
-  only containing nominal categorical features.
+  only containing categorical features.
   :pr:`802` by :user:`Guillaume Lemaitre <glemaitre>`.
 
 Enhancements

imblearn/metrics/pairwise.py

@@ -14,9 +14,9 @@
 class ValueDifferenceMetric(BaseEstimator):
     r"""Class implementing the Value Difference Metric.
 
-    This metric computes the distance between samples containing only nominal
-    features. The distance between feature values of two samples
-    is defined as:
+    This metric computes the distance between samples containing only
+    categorical features. The distance between feature values of two samples is
+    defined as:
 
     .. math::
        \delta(x, y) = \sum_{c=1}^{C} |p(c|x_{f}) - p(c|y_{f})|^{k} \ ,

imblearn/over_sampling/_adasyn.py

@@ -52,7 +52,7 @@ class ADASYN(BaseOverSampler):
 
     SMOTENC : Over-sample using SMOTE for continuous and categorical features.
 
-    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
+    SMOTEN : Over-sample using the SMOTE variant specifically for categorical
         features only.
 
     SVMSMOTE : Over-sample using SVM-SMOTE variant.

imblearn/over_sampling/_random_over_sampler.py

@@ -76,7 +76,7 @@ class RandomOverSampler(BaseOverSampler):
 
     SMOTENC : Over-sample using SMOTE for continuous and categorical features.
 
-    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
+    SMOTEN : Over-sample using the SMOTE variant specifically for categorical
         features only.
 
     SVMSMOTE : Over-sample using SVM-SMOTE variant.

imblearn/over_sampling/_smote.py

@@ -450,7 +450,7 @@ class SVMSMOTE(BaseSMOTE):
 
     SMOTENC : Over-sample using SMOTE for continuous and categorical features.
 
-    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
+    SMOTEN : Over-sample using the SMOTE variant specifically for categorical
         features only.
 
     BorderlineSMOTE : Over-sample using Borderline-SMOTE.
@@ -648,7 +648,7 @@ class SMOTE(BaseSMOTE):
     --------
     SMOTENC : Over-sample using SMOTE for continuous and categorical features.
 
-    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
+    SMOTEN : Over-sample using the SMOTE variant specifically for categorical
         features only.
 
     BorderlineSMOTE : Over-sample using the borderline-SMOTE variant.
@@ -743,7 +743,7 @@ def _fit_resample(self, X, y):
 class SMOTENC(SMOTE):
     """Synthetic Minority Over-sampling Technique for Nominal and Continuous.
 
-    Unlike :class:`SMOTE`, SMOTE-NC for dataset containing continuous and
+    Unlike :class:`SMOTE`, SMOTE-NC for dataset containing numerical and
     categorical features. However, it is not designed to work with only
     categorical features.
 
@@ -774,7 +774,7 @@ class SMOTENC(SMOTE):
     --------
     SMOTE : Over-sample using SMOTE.
 
-    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
+    SMOTEN : Over-sample using the SMOTE variant specifically for categorical
         features only.
 
     SVMSMOTE : Over-sample using SVM-SMOTE variant.
@@ -1068,7 +1068,7 @@ class KMeansSMOTE(BaseSMOTE):
 
     SMOTENC : Over-sample using SMOTE for continuous and categorical features.
 
-    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
+    SMOTEN : Over-sample using the SMOTE variant specifically for categorical
         features only.
 
     SVMSMOTE : Over-sample using SVM-SMOTE variant.
@@ -1272,9 +1272,10 @@ def _fit_resample(self, X, y):
     random_state=_random_state_docstring,
 )
 class SMOTEN(SMOTE):
-    """Perform SMOTE over-sampling for nominal categorical features only.
+    """Synthetic Minority Over-sampling Technique for Nominal.
 
-    This method is refered as SMOTEN in [1]_.
+    This method is refered as SMOTEN in [1]_. It expects that the data to
+    resample are only made of categorical features.
 
     Read more in the :ref:`User Guide <smote_adasyn>`.