didnt change

cnn.py

@@ -69,7 +69,7 @@ def find_classes(x):
 
     Parameters
     ----------
-    x : iterable 
+    x : iterable
         heterogeneous memberships.
 
     Returns
@@ -141,29 +141,29 @@ def balance(X,y,method):
     elif method == 'undersampling':
         print('UNDERSAMPLING')
         return undesample(X,y),None
-    
+
 class CNN_Antifrag:
     """
     Convolutional Neural Network
     For predicting Robustness and Evolvability
     Based on antifragility estimations
     """
-    
+
     def __init__(self,name='CNN',K = 5,N = 4):
         """
         Creates a Convolutional Neural Network
-        Modeling experiment 
-        
+        Modeling experiment
+
         Parameters
         ----------
         name: str,optional
             prefix for history files
             The default is 'CNN'
         K : int, optional
-            Number of folds in the cross validation. 
+            Number of folds in the cross validation.
             The default is 5.
         N : int, optional
-            Number of classes. 
+            Number of classes.
             The default is 4.
 
         Returns
@@ -175,10 +175,10 @@ class CNN_Antifrag:
         self.K = K
         self.N = N
         ioff()
-        
+
     def save_history_plots(self,history,outer,inner=None,name=None):
         """
-        
+
 
         Parameters
         ----------
@@ -221,11 +221,11 @@ class CNN_Antifrag:
         bs, ep = m.choose_batch_epochs(b,e)
         o = m.choose_balancing_method(o)
         K,N = self.K,self.N
-        
+
         # random states are defined for reproducibility of results
         outer = StratifiedKFold(K,shuffle=True,random_state=0xBBBB)
         inner = StratifiedKFold(K-1,shuffle=True,random_state=0xCCCC)
-    
+
         total_acc, total_auc = 0,0
         # outer loop splits test sets
         # Test data is never used in training or cross validation
@@ -239,45 +239,45 @@ class CNN_Antifrag:
             for (train_idx,val_idx),j in zip(inner.split(X_D,y_D),range(K-1)):
                 X_train,y_train = dims(X_D[train_idx],2),encode(y_D[train_idx])
                 X_val,y_val = dims(X_D[val_idx],2),encode(y_D[val_idx])
-    
+
                 # creating a new instance of the architecture
                 model = m.model_architecture(c)
                 # compile the keras model
-                model.compile(loss = 'categorical_crossentropy', 
-                              optimizer = 'adam', 
+                model.compile(loss = 'categorical_crossentropy',
+                              optimizer = 'adam',
                               metrics = ['accuracy'])
-                # model training, 3D expansion of the input required 
+                # model training, 3D expansion of the input required
                 # for convolutional layers
-                history = model.fit(X_train, 
-                          y_train, 
-                          batch_size = bs, 
-                          epochs = ep, 
-                          verbose = 2, 
+                history = model.fit(X_train,
+                          y_train,
+                          batch_size = bs,
+                          epochs = ep,
+                          verbose = 2,
                           class_weight=weights,
                           validation_data = (X_val, y_val))
-                
+
                 # save history of accuracy and loss
                 self.save_history_plots(history,i,j)
-                
+
                 # calculate accuracy
                 _,accuracy = model.evaluate(X_val, y_val,verbose = 0)
                 total_acc += accuracy
                 print("t_set = " + str(i) + " v_set = " + str(j))
                 print('Test accuracy:', accuracy)
-    
-    
+
+
                 # calculate area under the curve
                 y_pred = model.predict(X_val, batch_size = bs)
                 fpr, tpr, auc = ra.roc_auc(N, y_val, y_pred)
                 total_auc += auc
                 print("Area under the curve:", auc)
-    
-    
+
+
         total_acc = total_acc/(K*(K-1))
         total_auc = total_auc/(K*(K-1))
         print("Average accuracy: ", total_acc)
         print("Average area under the curve: ", total_auc)
-    
+
         return total_acc, total_auc
 
     def run_kfold(self,X, y, params):
@@ -286,10 +286,10 @@ class CNN_Antifrag:
         bs, ep = m.choose_batch_epochs(b,e)
         o = m.choose_balancing_method(o)
         K,N = self.K,self.N
-        
+
         # random states are defined for reproducibility of results
         kfold = StratifiedKFold(K,shuffle=True,random_state=0xBBBB)
-    
+
         precs_k, recs_k, avgs_k = [], [], []
 
         total_acc, total_auc = 0,0
@@ -302,33 +302,33 @@ class CNN_Antifrag:
             X_train,y_train = dims(X_train,2),encode(y_train)
             # test set is left imbalanced, one hot encoding for output
             (X_test,y_test) = dims(X[test_idx],2),encode(y[test_idx])
-            
+
             # creating a new instance of the architecture
             model = m.model_architecture(c)
             # compile the keras model
-            model.compile(loss = 'categorical_crossentropy', 
-                          optimizer = 'adam', 
+            model.compile(loss = 'categorical_crossentropy',
+                          optimizer = 'adam',
                           metrics = ['accuracy'])
-            # model training, 3D expansion of the input required 
+            # model training, 3D expansion of the input required
             # for convolutional layers
-            history = model.fit(X_train, 
-                      y_train, 
-                      batch_size = bs, 
-                      epochs = ep, 
-                      verbose = 2, 
+            history = model.fit(X_train,
+                      y_train,
+                      batch_size = bs,
+                      epochs = ep,
+                      verbose = 2,
                       class_weight=weights,
                       validation_data = (X_test, y_test))
-                
+
             # save history of accuracy and loss
             self.save_history_plots(history,i,name='Eval_'+self.name)
-            
+
             # calculate accuracy
             _,accuracy = model.evaluate(X_test, y_test,verbose = 0)
             total_acc += accuracy
             print("fold = " + str(i))
             print('Test accuracy:', accuracy)
-    
-    
+
+
             # calculate area under the curve
             y_pred = model.predict(X_test, batch_size = bs)
             fpr, tpr, auc = ra.roc_auc(N, y_test, y_pred)
@@ -342,14 +342,14 @@ class CNN_Antifrag:
             else:
                 cm += confusion_matrix(
                     y_test.argmax(axis=1), y_pred.argmax(axis=1))
-    
+
             #pr curve (contains 4 pr curves: one for each class)
             recall, precision, average_prec = create_pr(N, y_test, y_pred)
             recs_k.append(recall)
             precs_k.append(precision)
             avgs_k.append(average_prec)
-        
-        
+
+
         total_acc = total_acc/K
         total_auc = total_auc/K
         cm = cm/K
@@ -358,4 +358,3 @@ class CNN_Antifrag:
         print("Average area under the curve: ", total_auc)
 
         return total_acc, total_auc, cm, pr
-