added generic weighted average function

CHANGELOG.md

@@ -471,7 +471,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 [Unreleased]: https://github.com/sepandhaghighi/pycm/compare/v2.6...dev
 
 ### Added
-- Weighted average F1
+- Weighted average for various class stats
 [2.6]: https://github.com/sepandhaghighi/pycm/compare/v2.5...v2.6
 [2.5]: https://github.com/sepandhaghighi/pycm/compare/v2.4...v2.5
 [2.4]: https://github.com/sepandhaghighi/pycm/compare/v2.3...v2.4

Test/function_test.py

@@ -515,4 +515,7 @@
 0.4
 >>> cm3.LambdaB
 0.35714285714285715
+
+>>> cm.weighted_average("F2")
+0.5684946392350044
 """

pycm/pycm_obj.py

@@ -656,3 +656,26 @@ def relabel(self, mapping):
         self.FP = self.class_stat["FP"]
         self.FN = self.class_stat["FN"]
         __class_stat_init__(self)
+    def weighted_average(self, item, weights=None):
+        """
+        Calculate Weighted average of input parameter.
+
+        :param item: a class item for which weighted average has to be calculated
+        :type item1:str
+        :param weights: Explicitly passes weights or Support or P
+        :type item2:dict
+    
+        :return: weighted average of the input parameter
+        """
+        if not weights:
+            """ Set default weights to the Support or P"""
+            weights = list(self.class_stat["P"].values())
+        try:
+            item_values = list(self.class_stat[item].values())
+        except KeyError:
+            return "Error: Invalid item {}".format(item)
+        try:
+            return numpy.average(item_values,weights=weights)
+        except Exception:
+            return "None"
+

pycm/pycm_overall_func.py

@@ -659,22 +659,6 @@ def macro_calc(item):
     except Exception:
         return "None"
 
-def weighted_calc(item1,item2):
-    """
-    Calculate PPV_Macro and TPR_Macro.
-
-    :param item1: F1
-    :type item1:dict
-    :param item2: P or Support
-    :type item2:dict
-    
-    :return: weighted average F1
-    """
-    try:
-        return numpy.average(list(item1.values()),weights=list(item2.values()))
-    except Exception:
-        return "None"
-
 def PC_AC1_calc(P, TOP, POP):
     """
     Calculate percent chance agreement for Gwet's AC1.