evaluate.py

@@ -3,7 +3,6 @@ import pandas as pd
 import jsgf
 from unidecode import unidecode
 import string
-from collections import defaultdict
 
 
 def decode_prompt(prompt):
@@ -22,12 +21,6 @@ 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:
@@ -47,41 +40,7 @@ for df in data_files:
     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))
-
 
 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}")