Training, crossvalidation and testing dataset

@@ -191,39 +191,33 @@ if __name__ == "__main__":
     print("   Number of testing class I: {}".format(y_test.count('I')))
     print("   Shape of testing matrix: {}".format(X_test.shape))
 
-    jobs = -1
-    paramGrid = []
-    nIter = 20
-    crossV = 10
-    print("Defining randomized grid search...")
     if args.classifier == 'SVM':
         # SVM
-        classifier = SVC(args.kernel)
-    elif args.classifier == 'BernoulliNB':
+        myClassifier = SVC(args.kernel)
+    elif args.myClassifier == 'BernoulliNB':
         # BernoulliNB
-        classifier = BernoulliNB()
+        myClassifier = BernoulliNB()
     elif args.classifier == 'kNN':
         # kNN
-        k_range = list(range(1, 7, 2))
-        classifier = KNeighborsClassifier()
+        myClassifier = KNeighborsClassifier()
     else:
         print("Bad classifier")
         exit()
     print("   Done!")
 
     print("Training...")
-    classifier.fit(X_train, y_train)
+    myClassifier.fit(X_train, y_train)
     print("   Done!")
 
-    y_pred = classifier.predict(X_test)
-    best_parameters = classifier.best_estimator_.get_params()
+    y_pred = myClassifier.predict(X_test)
+    best_parameters = myClassifier.best_estimator_.get_params()
     print("   Done!")
 
     print("Saving report...")
     with open(os.path.join(args.outputReportPath, args.outputReportFile), mode='w', encoding='utf8') as oFile:
         oFile.write('**********        EVALUATION REPORT     **********\n')
         oFile.write('Reduction: {}\n'.format(args.reduction))
-        oFile.write('Classifier: {}\n'.format(args.classifier))
+        oFile.write('Classifier: {}\n'.format(args.myClassifier))
         oFile.write('Kernel: {}\n'.format(args.kernel))
         oFile.write('Accuracy: {}\n'.format(accuracy_score(y_test, y_pred)))
         oFile.write('Precision: {}\n'.format(precision_score(y_test, y_pred, average='weighted')))