fix

charmatch.py

@@ -88,33 +88,30 @@ class charmatch(evaluate.Metric):
         pass
 
     def _compute(self, inputs, expected, outputs):
+        def calculate_metric(t, dl_sh, dl_sg):
+            precision = sum(t) / sum(dl_sh)
+            recall = sum(t) / sum(dl_sg)
+            f_05 = (1 + 0.5**2) * ((precision * recall) / (0.5**2 * precision + recall))
+            return f_05
+
         def get_score(input, expected, output):
-            print(input, expected, output)
-            deduped = {input, expected, output}
-            if len(deduped) == 1:
-                return 1.0
-            elif len(deduped) == 2:
-                if expected == output:
-                    return 1.0
-                else:
-                    return 0.0
-            else:
-                expected_corrections = lev(input, expected)
-                distance_to_input = lev(input, output)
-                distance_to_expected = lev(output, expected)
-                print(f'dl(s,g): {expected_corrections}\ndl(s,h): {distance_to_input}\ndl(h,g): {distance_to_expected}')
-
-                true_positives = min(expected_corrections, max(0, (expected_corrections + distance_to_input - distance_to_expected))) / 2
-                print(f'T: {true_positives}')
-
-                precision = true_positives / distance_to_input
-                recall = true_positives / expected_corrections
-                f_05 = (1 + 0.5**2) * (precision * recall) / (0.5**2 * precision + recall)
-                print(f'P: {precision}\nR: {recall}')
-
-                return f_05
-
-        avg = sum([get_score(*row) for row in zip(inputs, expected, outputs)]) / len(inputs) * 100
+            expected_corrections = lev(input, expected)
+            distance_to_input = lev(input, output)
+            distance_to_expected = lev(output, expected)
+            #true_positives = abs((expected_corrections + distance_to_input - distance_to_expected) / 2)
+            true_positives = min(expected_corrections, max(0, (expected_corrections + distance_to_input - distance_to_expected)) / 2)
+            return true_positives, distance_to_input, expected_corrections
+
+        t_list = []
+        dl_sh_list = []
+        dl_sg_list = []
+        for row in zip(inputs, expected, outputs):
+            score = get_score(*row)
+            t_list.append(score[0])
+            dl_sh_list.append(score[1])
+            dl_sg_list.append(score[2])
+
+        avg = calculate_metric(t_list, dl_sh_list, dl_sg_list)
 
         return {
             "fscore": avg