.gitignore

@@ -1,3 +1,2 @@
 __pycache__/
-datasets/
-swap_results.csv
+datasets/

correlation.py

@@ -55,22 +55,19 @@ if __name__ == "__main__":
 
     results = []
 
-    # nasbench_len = 15625
     nasbench_len = 15625
     
     # for index, i in arch_info.iterrows():
-    for ind in range(nasbench_len):
+    for i in range(nasbench_len):
         # print(f'Evaluating network: {index}')
-        print(f'Evaluating network: {ind}')
+        print(f'Evaluating network: {i}')
 
-        config = api.get_net_config(ind, 'cifar10')
+        config = api.get_net_config(i, 'cifar10')
         network = get_cell_based_tiny_net(config)
-        # nas_results = api.query_by_index(i, 'cifar10')
-        # acc = nas_results[111].get_eval('ori-test')
-        nas_results = api.get_more_info(ind, 'cifar10', None, hp=200, is_random=False)
-        acc = nas_results['test-accuracy']
+        nas_results = api.query_by_index(i, 'cifar10')
+        acc = nas_results[111].get_eval('ori-test')
 
-        # print(type(network))
+        print(type(network))
         start_time = time.time()
 
         # network = Network(3, 10, 1, eval(i.genotype))
@@ -96,13 +93,13 @@ if __name__ == "__main__":
         print(f'Average SWAP score: {np.mean(swap_score)}')
         print(f'Elapsed time: {end_time - start_time:.2f} seconds')
 
-        results.append([np.mean(swap_score), acc, ind])
+        results.append([np.mean(swap_score), acc, i])
 
     results = pd.DataFrame(results, columns=['swap_score', 'valid_acc', 'index'])
-    results.to_csv('output/swap_results.csv', float_format='%.4f', index=False)
 
     print()    
     print(f'Spearman\'s Correlation Coefficient: {stats.spearmanr(results.swap_score, results.valid_acc)[0]}')
+    results.to_csv('swap_results.csv', float_format='%.4f', index=False)