Training, crossvalidation and testing dataset

@@ -210,15 +210,14 @@ if __name__ == "__main__":
     print("   Done!")
 
     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.myClassifier))
         oFile.write('Kernel: {}\n'.format(args.kernel))
+        oFile.write('Training score: {}\n'.format(myClassifier.score()))
         oFile.write('Accuracy: {}\n'.format(accuracy_score(y_test, y_pred)))
         oFile.write('Precision: {}\n'.format(precision_score(y_test, y_pred, average='weighted')))
         oFile.write('Recall: {}\n'.format(recall_score(y_test, y_pred, average='weighted')))
@@ -227,9 +226,6 @@ if __name__ == "__main__":
         oFile.write(str(confusion_matrix(y_test, y_pred)) + '\n')
         oFile.write('Classification report: \n')
         oFile.write(classification_report(y_test, y_pred) + '\n')
-        oFile.write('Best parameters: \n')
-        for param in sorted(best_parameters.keys()):
-            oFile.write("\t%s: %r\n" % (param, best_parameters[param]))
     print("   Done!")
 
     print("Training and testing done in: %fs" % (time() - t0))