Training, crossvalidation and testing dataset

@@ -157,38 +157,6 @@ if __name__ == "__main__":
     print("   Number of training class I: {}".format(y_train.count('I')))
     print("   Number of training class I: {}".format(y_train.count('I')))
     print("   Shape of training matrix: {}".format(X_train.shape))
     print("   Shape of training matrix: {}".format(X_train.shape))
 
 
-    print("Reading testing data and true classes...")
-    X_test = None
-    if args.saveData:
-        y_test = []
-        testingData = []
-        with open(os.path.join(args.inputPath, args.inputTestingData), encoding='utf8', mode='r') \
-                as iFile:
-            for line in iFile:
-                line = line.strip('\r\n')
-                listLine = line.split(',')
-                testingData.append(listLine[1:])
-        X_test = csr_matrix(testingData, dtype='double')
-        with open(os.path.join(args.inputPath, args.inputTestingClasses), encoding='utf8', mode='r') \
-                as iFile:
-            for line in iFile:
-                line = line.strip('\r\n')
-                y_test.append(line)
-        print("   Saving matrix and classes...")
-        joblib.dump(X_test, os.path.join(args.outputModelPath, args.inputTestingData + '.jlb'))
-        joblib.dump(y_test, os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb'))
-        print("      Done!")
-    else:
-        print("   Loading matrix and classes...")
-        X_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingData + '.jlb'))
-        y_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb'))
-        print("      Done!")
-
-    print("   Number of testing classes: {}".format(len(y_test)))
-    print("   Number of testing class A: {}".format(y_test.count('A')))
-    print("   Number of testing class I: {}".format(y_test.count('I')))
-    print("   Shape of testing matrix: {}".format(X_test.shape))
-
     # Feature selection and dimensional reduction
     # Feature selection and dimensional reduction
     if args.reduction is not None:
     if args.reduction is not None:
         print('Performing dimensionality reduction or feature selection...', args.reduction)
         print('Performing dimensionality reduction or feature selection...', args.reduction)
@@ -252,11 +220,43 @@ if __name__ == "__main__":
         X_train, y_train = sm.fit_sample(X_train, y_train)
         X_train, y_train = sm.fit_sample(X_train, y_train)
 
 
         print("         After transformtion with {}".format(args.imbalanced))
         print("         After transformtion with {}".format(args.imbalanced))
+        print("   Number of training classes: {}".format(len(y_train)))
+        print("   Number of training class A: {}".format(y_train.count('A')))
+        print("   Number of training class I: {}".format(y_train.count('I')))
+        print("   Shape of training matrix: {}".format(X_train.shape))
+        print("      Data transformation done in : %fs" % (time() - t1))
+
+    print("Reading testing data and true classes...")
+    X_test = None
+    if args.saveData:
+        y_test = []
+        testingData = []
+        with open(os.path.join(args.inputPath, args.inputTestingData), encoding='utf8', mode='r') \
+                as iFile:
+            for line in iFile:
+                line = line.strip('\r\n')
+                listLine = line.split(',')
+                testingData.append(listLine[1:])
+        X_test = csr_matrix(testingData, dtype='double')
+        with open(os.path.join(args.inputPath, args.inputTestingClasses), encoding='utf8', mode='r') \
+                as iFile:
+            for line in iFile:
+                line = line.strip('\r\n')
+                y_test.append(line)
+        print("   Saving matrix and classes...")
+        joblib.dump(X_test, os.path.join(args.outputModelPath, args.inputTestingData + '.jlb'))
+        joblib.dump(y_test, os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb'))
+        print("      Done!")
+    else:
+        print("   Loading matrix and classes...")
+        X_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingData + '.jlb'))
+        y_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb'))
+        print("      Done!")
+
     print("   Number of testing classes: {}".format(len(y_test)))
     print("   Number of testing classes: {}".format(len(y_test)))
     print("   Number of testing class A: {}".format(y_test.count('A')))
     print("   Number of testing class A: {}".format(y_test.count('A')))
     print("   Number of testing class I: {}".format(y_test.count('I')))
     print("   Number of testing class I: {}".format(y_test.count('I')))
     print("   Shape of testing matrix: {}".format(X_test.shape))
     print("   Shape of testing matrix: {}".format(X_test.shape))
-        print("      Data transformation done in : %fs" % (time() - t1))
 
 
     jobs = -1
     jobs = -1
     paramGrid = []
     paramGrid = []