Merge branch 'main' of https://git.wmi.amu.edu.pl/s464915/JARVIS

evaluate.py

@@ -28,6 +28,8 @@ 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:
@@ -40,48 +42,33 @@ for df in data_files:
     user_speeches = user_speech_rows["message"]
     entries_count = len(user_speeches)
 
-
-    parsed = user_speeches.apply(
-        lambda x: bool(grammar.find_matching_rules(decode_prompt(x))))
+    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))))
     true_count = parsed.sum()
     false_count = len(parsed) - true_count
     recognized += true_count
     unrecognized += false_count
     
-    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))
+    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
 
+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: {recognized/(recognized+unrecognized)}")
+print(f"Accuracy: {accuracy}")
+print(f"Precision: {precision}")
+print(f"Recall: {recall}")
 
-
-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}")