evaluate.py

@@ -28,8 +28,6 @@ false_negatives = 0
 
 acts_recognized = defaultdict(int)
 acts_not_recognized = defaultdict(int)
-false_negatives = 0
-false_positives = 0
 
 for df in data_files:
     if len(df.columns) == 3:
@@ -42,33 +40,48 @@ for df in data_files:
     user_speeches = user_speech_rows["message"]
     entries_count = len(user_speeches)
 
-    found_rules = user_speeches.apply(lambda x: grammar.find_matching_rules(decode_prompt(x)))
-    parsed = user_speeches.apply(lambda x: bool(grammar.find_matching_rules(decode_prompt(x))))
+
+    parsed = user_speeches.apply(
+        lambda x: bool(grammar.find_matching_rules(decode_prompt(x))))
     true_count = parsed.sum()
     false_count = len(parsed) - true_count
     recognized += true_count
     unrecognized += false_count
     
-    for line, rules in zip(df.iterrows(), found_rules):
-        act = line[1]['act'].split('(')[0]
-        if len(rules) > 0:
-            recognized_act = rules[0].name
-            if recognized_act in act:
-                true_positives += 1
-            else:
-                false_positives += 1
-                acts_not_recognized[act] += 1
-        else:
-            false_negatives += 1
-            acts_not_recognized[act] += 1
+    for line, correct in zip(df.iterrows(), parsed):
+        acts_recognized[line[1]['act'].split('(')[0]] += int(correct)
+        acts_not_recognized[line[1]['act'].split('(')[0]] += int(not(correct))
 
-accuracy = recognized / (recognized + unrecognized)
-precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) != 0 else 0
-recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) != 0 else 0
 
 print(f"Recognized user utterances: {recognized}")
 print(f"Unrecognized user utterances: {unrecognized}")
-print(f"Accuracy: {accuracy}")
-print(f"Precision: {precision}")
-print(f"Recall: {recall}")
+print(f"Accuracy: {recognized/(recognized+unrecognized)}")
 
+
+precision_per_class = {}
+recall_per_class = {}
+
+for act in acts_recognized.keys():
+    true_positives = acts_recognized[act]
+    false_negatives = acts_not_recognized[act]
+    false_positives = recognized - true_positives
+
+    precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) != 0 else 0
+    recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) != 0 else 0
+
+    precision_per_class[act] = precision
+    recall_per_class[act] = recall
+
+average_precision = sum(precision_per_class.values()) / len(precision_per_class)
+average_recall = sum(recall_per_class.values()) / len(recall_per_class)
+
+print("\nPrecision per class:")
+for act, precision in precision_per_class.items():
+    print(f"{act}: {precision}")
+
+print("\nRecall per class:")
+for act, recall in recall_per_class.items():
+    print(f"{act}: {recall}")
+
+print(f"\nAverage Precision: {average_precision}")
+print(f"Average Recall: {average_recall}")