Adds probability difference to copycat/statistics

copycat/statistics.py

@@ -24,17 +24,18 @@ _ptable = {
         16:26.3
         }
 
+
+_get_count = lambda k, d : d[k]['count'] if k in d else 0
+
 def g_value(actual, expected):
     # G = 2 * sum(Oi * ln(Oi/Ei))
     answerKeys = set(list(actual.keys()) + list(expected.keys()))
     degreesFreedom = len(answerKeys)
     G = 0
 
-    get_count = lambda k, d : d[k]['count'] if k in d else 0
-
     for k in answerKeys:
-        E = get_count(k, expected)
-        O = get_count(k, actual)
+        E = _get_count(k, expected)
+        O = _get_count(k, actual)
         if E == 0:
             print('    Warning! Expected 0 counts of {}, but got {}'.format(k, O))
         elif O == 0:
@@ -49,17 +50,39 @@ def chi_value(actual, expected):
     degreesFreedom = len(answerKeys)
     chiSquared = 0
 
-    get_count = lambda k, d : d[k]['count'] if k in d else 0
-
     for k in answerKeys:
-        E = get_count(k, expected)
-        O = get_count(k, actual)
+        E = _get_count(k, expected)
+        O = _get_count(k, actual)
         if E == 0:
             print('    Warning! Expected 0 counts of {}, but got {}'.format(k, O))
         else:
             chiSquared += (O - E) ** 2 / E
     return degreesFreedom, chiSquared
 
+def probability_difference(actual, expected):
+    actualC   = 0
+    expectedC = 0
+
+    for k in set(list(actual.keys()) + list(expected.keys())):
+        expectedC += _get_count(k, expected)
+        actualC   += _get_count(k, actual)
+
+    p = 0
+
+    Et = 0
+    Ot = 0
+
+    for k in set(list(actual.keys()) + list(expected.keys())):
+        E = _get_count(k, expected)
+        O = _get_count(k, actual)
+        Ep = E / expectedC
+        Op = O / actualC
+        p += abs(Ep - Op)
+
+    p /= 2 # P is between 0 and 2 -> P is between 0 and 1
+
+    return p
+
 def dist_test(actual, expected, calculation):
     df, p = calculation(actual, expected)
     if df not in _ptable:
@@ -67,14 +90,17 @@ def dist_test(actual, expected, calculation):
                         ' Please look up the value and add it to the table in copycat/statistics.py'.format(df))
     return (p < _ptable[df])
 
-def cross_formula_table(actualDict, expectedDict, calculation):
+def cross_formula_table(actualDict, expectedDict, calculation, probs=False):
     data = dict()
     for ka, actual in actualDict.items():
         for ke, expected in expectedDict.items():
+            if probs:
+                data[(ka, ke)] = probability_difference(actual, expected)
+            else:
                 data[(ka, ke)] = dist_test(actual, expected, calculation)
     return data
 
-def cross_table(problemSets, calculation=g_value):
+def cross_table(problemSets, calculation=g_value, probs=False):
     table = defaultdict(dict)
     for i, (a, problemSetA) in enumerate(problemSets):
         for b, problemSetB in problemSets[i + 1:]:
@@ -89,7 +115,7 @@ def cross_table(problemSets, calculation=g_value):
                                problemA.modified,
                                problemA.target)][(a, b)] = (
                                        cross_formula_table(
-                                       answersA, answersB, calculation))
+                                       answersA, answersB, calculation, probs))
     return table
 
 def iso_chi_squared(actualDict, expectedDict):

cross_compare.py

@@ -19,7 +19,7 @@ def main(args):
             pSet = pickle.load(infile)
             branchProblemSets[filename] = pSet
             problemSets.append((filename, pSet))
-    crossTable = cross_table(problemSets)
+    crossTable = cross_table(problemSets, probs=True)
     key_sorted_items = lambda d : sorted(d.items(), key=lambda t:t[0])
 
     tableItems = key_sorted_items(crossTable)