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code/evaluation.py

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+import argparse
+import json
+import pickle
+from tqdm import tqdm
+from pathlib import Path
+import re
+
+def string_match(answer, prediction, choices):
+    # Function to normalize and tokenize text
+    def tokenize(text):
+        # Convert to lowercase and find all word tokens
+        return set(re.findall(r'\b\w+\b', text.lower()))
+    
+    # Tokenize prediction and answer
+    prediction_tokens = tokenize(prediction)
+    answer_tokens = tokenize(answer)
+    
+    if not prediction_tokens:
+        return False
+    
+    # Tokenize incorrect choices and exclude tokens present in the answer
+    incorrect_tokens = set()
+    for choice in choices:
+        choice_tokens = tokenize(choice)
+        if choice_tokens != answer_tokens:
+            incorrect_tokens.update(choice_tokens - answer_tokens)
+    
+    # Condition 1: All tokens of the answer are in the prediction
+    cond1 = answer_tokens.issubset(prediction_tokens)
+    
+    # Condition 2: Prediction does not contain any tokens from incorrect choices (excluding shared words)
+    cond2 = prediction_tokens.isdisjoint(incorrect_tokens)
+    
+    return cond1 and cond2
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description="Process benchmark JSON and calculate accuracy.")
+    parser.add_argument('--input', type=str, required=True, help='Path to input JSON file to be evaluated')
+    
+    args = parser.parse_args()  
+    
+    with open(args.input, 'r') as f:
+        input_data = json.load(f)
+
+    corr, total = 0, 0
+
+    # Track metrics for different categories:
+    modality_metrics = {'sound': [0, 0], 'music': [0, 0], 'speech': [0, 0], 'mix-sound-music': [0, 0], 'mix-sound-speech': [0, 0], 'mix-music-speech': [0, 0], 'mix-sound-music-speech': [0, 0]}
+    category_metrics = {'Signal Layer': [0, 0], 'Perception Layer': [0, 0], 'Semantic Layer': [0, 0], 'Cultural Layer': [0, 0]}
+    
+    # Here is the new dict for sub-category metrics
+    subcat_metrics = {}
+
+    output_key = 'model_prediction' # The key that contains model output
+    no_pred_count = 0
+    matched_outputs = []
+    new_data = []
+
+    # for idx, sample in enumerate(tqdm(input_data)):
+    for idx, sample in enumerate(input_data):
+        
+        # If there's no model output key, skip
+        if output_key not in sample:
+            continue
+        
+        if output_key not in sample:
+            _prediction = ''
+            no_pred_count += 1
+        else:
+            _prediction = sample[output_key]
+
+        _answer = sample['answer']
+        modality = sample['modality']
+        category = sample['category']
+        choices = sample['choices']
+        
+        # Get the sub-category
+        subcat = sample.get('sub-category', None)
+        if subcat is not None:
+            # If we haven't seen this sub-category before, initialize
+            if subcat not in subcat_metrics:
+                subcat_metrics[subcat] = [0, 0]
+
+        match_result = string_match(_answer, _prediction, choices)
+
+        if match_result:
+            modality_metrics[modality][0] += 1
+            category_metrics[category][0] += 1
+            if subcat is not None:
+                subcat_metrics[subcat][0] += 1
+            matched_outputs.append([_answer, _prediction])
+            corr += 1
+            sample['match'] = 1
+        else:
+            sample['match'] = 0
+
+        total += 1
+        new_data.append(sample)
+        modality_metrics[modality][1] += 1
+        category_metrics[category][1] += 1
+        if subcat is not None:
+            subcat_metrics[subcat][1] += 1
+
+
+    # Print results:
+    print("*"*30)
+    print("Modality-wise Accuracy:")
+    for modality in modality_metrics:
+        n_correct, n_total = modality_metrics[modality]
+        acc = (n_correct / n_total) * 100 if n_total > 0 else 0
+        print(f"{modality} : {acc:.2f}% over {n_total} samples")
+    
+    print("*"*30)
+    print("Category-wise Accuracy:")
+    for category in category_metrics:
+        n_correct, n_total = category_metrics[category]
+        acc = (n_correct / n_total) * 100 if n_total > 0 else 0
+        print(f"{category} : {acc:.2f}% over {n_total} samples")
+    
+    print("*"*30)
+    print("Sub-category-wise Accuracy:")
+    for subcat in subcat_metrics:
+        n_correct, n_total = subcat_metrics[subcat]
+        acc = (n_correct / n_total) * 100 if n_total > 0 else 0
+        print(f"{subcat} : {acc:.2f}% over {n_total} samples")
+
+    print("*"*30)
+    print(f"Total Accuracy: {(corr/total) * 100:.2f}% over {total} samples")
+    print("*"*30)
+    print(f"No prediction count: {no_pred_count}")