PEP257 D203 D107 D212 #195

Author: dpuenteramirez

instance_selection/_CNN.py

@@ -23,6 +23,7 @@ class CNN:
     """
 
     def __init__(self):
+        """A constructor for the class."""
         self.x_attr = None
 
     def filter(self, samples, y):

instance_selection/_DROP3.py

@@ -34,6 +34,16 @@ class DROP3:
     """
 
     def __init__(self, nearest_neighbors=3, power_parameter=2):
+        """
+        The function takes in two parameters, nearest_neighbors and
+        power_parameter, and assigns them to the attributes nearest_neighbors
+        and power_parameter
+
+        :param nearest_neighbors: The number of nearest neighbors to use when
+        calculating the weights, defaults to 3 (optional)
+        :param power_parameter: This is the exponent that is used to calculate
+        the weights, defaults to 2 (optional)
+        """
         self.nearest_neighbors = nearest_neighbors
         self.power_parameter = power_parameter
         self.x_attr = None

instance_selection/_ENN.py

@@ -32,6 +32,16 @@ class ENN:
     """
 
     def __init__(self, nearest_neighbors=3, power_parameter=2):
+        """
+        The function takes in two parameters, nearest_neighbors
+        and power_parameter, and assigns them to the attributes
+        nearest_neighbors and power_parameter
+
+        :param nearest_neighbors: The number of nearest neighbors to use when
+        calculating the weights, defaults to 3 (optional)
+        :param power_parameter: This is the exponent that is used to calculate
+        the weights, defaults to 2 (optional)
+        """
         self.nearest_neighbors = nearest_neighbors
         self.power_parameter = power_parameter
         self.x_attr = None

instance_selection/_ICF.py

@@ -15,6 +15,7 @@
 
 
 class ICF:
+
     """
     Brighton, H., & Mellish, C. (2002). Advances in instance selection for
     instance-based learning algorithms. Data mining and knowledge
@@ -33,6 +34,16 @@ class ICF:
     """
 
     def __init__(self, nearest_neighbors=3, power_parameter=2):
+        """
+        The function takes in two parameters, nearest_neighbors and
+        power_parameter, and assigns them to the attributes nearest_neighbors
+        and power_parameter.
+
+        :param nearest_neighbors: The number of nearest neighbors to use when
+        calculating the weights, defaults to 3 (optional)
+        :param power_parameter: This is the exponent that is used to calculate
+        the weights, defaults to 2 (optional)
+        """
         self.nearest_neighbors = nearest_neighbors
         self.power_parameter = power_parameter
         self.x_attr = None

instance_selection/_LocalSets.py

@@ -12,6 +12,7 @@
 
 
 class LocalSets:
+
     """
     Leyva, E., González, A., & Pérez, R. (2015). Three new instance selection
     methods based on local sets: A comparative study with several approaches
@@ -23,6 +24,7 @@ class LocalSets:
     """
 
     def __init__(self):
+        """A constructor for the class."""
         self.local_sets = None
         self.n_id = 0
 
@@ -117,6 +119,7 @@ class LSSm(LocalSets):
     """
 
     def __init__(self):
+        """A constructor for the class."""
         super().__init__()
 
     def filter(self, instances, labels):
@@ -170,6 +173,7 @@ class LSBo(LocalSets):
     """
 
     def __init__(self):
+        """A constructor for the class."""
         super(LSBo, self).__init__()
 
     def filter(self, instances, labels):

instance_selection/_MSS.py

@@ -14,6 +14,7 @@
 
 
 class MSS:
+
     """
     Barandela, R., Ferri, F. J., & Sánchez, J. S. (2005). Decision boundary
     preserving prototype selection for nearest neighbor classification.
@@ -26,6 +27,7 @@ class MSS:
     """
 
     def __init__(self):
+        """A constructor for the class."""
         self.x_attr = None
 
     def filter(self, samples, y):

instance_selection/_RNN.py

@@ -26,6 +26,7 @@ class RNN:
     """
 
     def __init__(self):
+        """A constructor for the class."""
         self.x_attr = None
 
     def filter(self, samples, y):

instance_selection/__init__.py

@@ -1,5 +1,4 @@
-"""
-Instance Selection.
+"""Instance Selection.
 
 The package contains some of the most widely used instance selection algorithms
 in the literature.