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import os | |
import json | |
import logging | |
from scripts.get_prediction_result import get_prediction_result | |
from scripts.helper import ensure_directory_exists | |
# Set up logging configuration | |
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
# Improved function to evaluate noise robustness | |
def evaluate_information_integration(config): | |
result_path = config['result_path'] + 'Information Integration/' | |
noise_rate = config['noise_rate'] | |
model_name = config['model_name'] | |
# Iterate over each model specified in the config | |
filename = os.path.join(result_path, f"prediction_{config['output_file_extension']}.json") | |
ensure_directory_exists(filename) | |
results = get_prediction_result(config, config['integration_file_name'], filename) # Store results for this model | |
# Save results to a file | |
with open(filename, 'w', encoding='utf-8') as f: | |
for result in results: | |
f.write(json.dumps(result, ensure_ascii=False) + '\n') | |
# Compute per-model noise robustness | |
correct_count = sum(1 for res in results if 0 not in res['label'] and 1 in res['label']) | |
accuracy = correct_count / len(results) if results else 0 | |
# Calculate tt and all_rate metrics | |
tt = sum(1 for i in results if (noise_rate == 1 and i['label'][0] == -1) or (0 not in i['label'] and 1 in i['label'])) | |
all_rate = tt / len(results) if results else 0 | |
# Save the final score file with tt and all_rate | |
scores = { | |
'model': model_name, | |
'accuracy': accuracy, | |
'noise_rate': noise_rate, | |
'correct_count': correct_count, | |
'total': len(results), | |
'all_rate': all_rate, | |
'tt': tt | |
} | |
logging.info(f"Information IntegrationScore: {scores}") | |
logging.info(f"Accuracy: {accuracy:.2%}") | |
score_filename = os.path.join(result_path, f"scores_{config['output_file_extension']}.json") | |
with open(score_filename, 'w') as f: | |
json.dump(scores, f, ensure_ascii=False, indent=4) | |
return results | |