Comparison Plot

main.py

@@ -18,17 +18,17 @@ def main():
     logging.info('Started the Logging')
     start_time = time.time()
     # try:
-    trainX, trainY = IOHelper.get_mat_data(config['data_dir'], verbose=True)
+    #trainX, trainY = IOHelper.get_mat_data(config['data_dir'], verbose=True)
 
-    if config['model'] == 'eegnet' or config['model'] == 'eegnet_cluster':
-        trainX = np.transpose(trainX, (0, 2, 1))
-        logging.info(trainX.shape)
+    #if config['model'] == 'eegnet' or config['model'] == 'eegnet_cluster':
+    #    trainX = np.transpose(trainX, (0, 2, 1))
+    #    logging.info(trainX.shape)
 
     # tune(trainX,trainY)
 
-    run(trainX,trainY)
+    # run(trainX,trainY)
     # select_best_model()
-    # comparison_plot()
+    comparison_plot()
 
     logging.info("--- Runtime: %s seconds ---" % (time.time() - start_time))
     logging.info('Finished Logging')

utils/utils.py

@@ -70,7 +70,8 @@ def cp_dir(source, target):
 
 def comparison_plot():
 
-    run_dir = os.getcwd()+'/results/'
+    run_dir = './results/OHBM/'
+    print(run_dir)
     plt.figure()
     plt.title('Comparison of the Validation accuracy' )
     plt.grid(True)
@@ -78,18 +79,11 @@ def comparison_plot():
     plt.ylabel('accuracy (%)')
     
     for experiment in os.listdir(run_dir):
-        position = experiment.find('ensemble')
-        if position != -1:
-
-            name_split = experiment.split('_')
-            name=name_split[1]
-            for i in range(2,len(name_split)):
-                name += '_'
-                name += name_split[i]
-
-            summary = pd.read_csv(run_dir+experiment+'/'+name+'_history.csv')
-            acc = 100 * summary['val_accuracy']
-            plt.plot(acc,'-',label=name)
+        name = experiment
+        print(name)
+        summary = pd.read_csv(run_dir+experiment+'/'+name+'_history.csv')
+        acc = 100 * summary['val_accuracy']
+        plt.plot(acc, '-' , label=name)
 
     plt.legend()
     plt.savefig(run_dir+'/comparison_accuracy.png')