add precision & recall

evaluate.py

@@ -3,6 +3,7 @@ import pandas as pd
 import jsgf
 from unidecode import unidecode
 import string
+from collections import defaultdict
 
 
 def decode_prompt(prompt):
@@ -21,6 +22,12 @@ for filename in os.listdir("data"):
 
 recognized = 0
 unrecognized = 0
+true_positives = 0
+false_positives = 0
+false_negatives = 0
+
+acts_recognized = defaultdict(int)
+acts_not_recognized = defaultdict(int)
 
 for df in data_files:
     if len(df.columns) == 3:
@@ -41,6 +48,40 @@ for df in data_files:
     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))
+
+
 print(f"Recognized user utterances: {recognized}")
 print(f"Unrecognized user utterances: {unrecognized}")
 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}")