new parameters to main method

main.py

-import cnn
+from cnn import CNN_Antifrag
 from parser import parse_data
 from parser import parse_data
 import itertools
 import itertools
 from confusion_matrix import plot_confusion_matrix
 from confusion_matrix import plot_confusion_matrix
 
 
 """
 """
-(c,b,e) will be read from the command line or a script
-(c,b,e) corresponds to the combinations of the specific hyperparameters to build the model
+(c,b,e,o) will be read from the command line or a script
+(c,b,e,o) corresponds to the combinations of the specific hyperparameters 
+to build the model
 c belongs to {0,1,2,3} and represents the layer architecture
 c belongs to {0,1,2,3} and represents the layer architecture
 b belongs to {0,1} and represents the batch size
 b belongs to {0,1} and represents the batch size
 e belongs to {0,1} and represents the number of epochs
 e belongs to {0,1} and represents the number of epochs
+o belongs to {0,1,2} and represents the balancing method
 """
 """
-#reading arguments from command line
-
-#c = int(sys.argv[1])
-#b = int(sys.argv[2])
-#e = int(sys.argv[3])
 
 
 c_ = [0,1,2,3]
 c_ = [0,1,2,3]
-b_ = [0,1]
-e_ = [0,1]
-n_experiences = 10001
-combinations = itertools.product(c_,b_,e_)
+b_ = [1]
+e_ = [0]
+o_ = [0,1,2]
 
 
+n_experiences = 100
+combinations = itertools.product(c_,b_,e_,o_)
 
 
-#parse the data
-input_, output_ = parse_data(n_experiences)
 
 
-#run an specific combination
-max_params = (0,0,0)
+#parse the data
+input_, output_ = parse_data(n_experiences,kind='linear')
+#%%
 max_avg_auc = 0
 max_avg_auc = 0
 
 
 for params in combinations:
 for params in combinations:
-    avg_acc, avg_auc, X_train_kfold, X_test_kfold, y_train_kfold, y_test_kfold = cnn.run_nn(input_, output_, n_experiences, params)
+    cnn = CNN_Antifrag(name="CNN_%d_%d_%d_%d"%params)
+    avg_acc, avg_auc  = cnn.run_nn(input_, output_, params)
     if avg_auc > max_avg_auc:
     if avg_auc > max_avg_auc:
         max_avg_auc = avg_auc
         max_avg_auc = avg_auc
         max_params = params
         max_params = params
-        X_train_kfold_opt = X_train_kfold
-        X_test_kfold_opt = X_test_kfold
-        y_train_kfold_opt = y_train_kfold
-        y_test_kfold_opt = y_test_kfold
         
         
+#%%
+print("Best params:",max_params)
 # once we have chosen the optimal parameters we can do the normal kfold
 # once we have chosen the optimal parameters we can do the normal kfold
-
-#note: the test data remains unbalanced
-acc, auc, cm, pr = cnn.run_kfold(X_train_kfold_opt, X_test_kfold_opt, y_train_kfold_opt, y_test_kfold_opt, max_params)
+cnn = CNN_Antifrag(name="CNN_%d_%d_%d_%d"%max_params)
+acc, auc, cm, pr = cnn.run_kfold(input_, output_, max_params)
 #to add: precision recall curve
 #to add: precision recall curve
 
 
-
-labels = ['~robust&~evolvable', 'evolvable&~robust', 'robust&~evolvable', 'robust&evolvable']
-plot_confusion_matrix(cm, labels) #this function saves the matrix image automatically
-
-f = open("acc_auc.txt", 'w+')
+#%%
+labels = [
+    '[~R & ~E]', 
+    '[~R &  E]', 
+    '[ R & ~E]', 
+    '[ R &  E]'
+    ]
+#this function saves the matrix image automatically
+plot_confusion_matrix(cm, labels) 
+
+f = open("out/acc_auc.txt", 'w+')
 f.write("Average accuracy: " + str(acc)+"\n")
 f.write("Average accuracy: " + str(acc)+"\n")
 f.write("Average area under the curve: " + str(auc))
 f.write("Average area under the curve: " + str(auc))
 f.close()
 f.close()
 
 
-## TO DO: code that allows to execute in parallel, make sure it's the same random shuffle ...