{"cells":[{"metadata":{},"cell_type":"markdown","source":"This kernel is 100% inspired in this <a href=\"https://www.kaggle.com/tarunpaparaju/how-top-lb-got-their-score-use-titanic-to-learn\">notebook</a> that shows how users got 100% scores in the titanic competition and I will do it for the digit recognizer one."},{"metadata":{},"cell_type":"markdown","source":"I will show how simple it is to get 100% score in the MNIST competition by just getting the ground truth labels and how unfair this is with the other competitors."},{"metadata":{"_cell_guid":"","_uuid":"","trusted":true},"cell_type":"code","source":"import pandas as pd\nmnist_test = pd.read_csv(\"../input/mnist-in-csv/mnist_test.csv\")\nmnist_train = pd.read_csv(\"../input/mnist-in-csv/mnist_train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"","_uuid":"","trusted":true},"cell_type":"code","source":"sample_submission = pd.read_csv(\"../input/digit-recognizer/sample_submission.csv\")\ntest = pd.read_csv(\"../input/digit-recognizer/test.csv\")\ntrain = pd.read_csv(\"../input/digit-recognizer/train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cols = test.columns","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test['dataset'] = 'test'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['dataset'] = 'train'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dataset = pd.concat([train.drop('label', axis=1), test]).reset_index()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mnist = pd.concat([mnist_train, mnist_test]).reset_index(drop=True)\nlabels = mnist['label'].values\nmnist.drop('label', axis=1, inplace=True)\nmnist.columns = cols","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"idx_mnist = mnist.sort_values(by=list(mnist.columns)).index\ndataset_from = dataset.sort_values(by=list(mnist.columns))['dataset'].values\noriginal_idx = dataset.sort_values(by=list(mnist.columns))['index'].values","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(len(idx_mnist)):\n    if dataset_from[i] == 'test':\n        sample_submission.loc[original_idx[i], 'Label'] = labels[idx_mnist[i]]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"That's it! You can get perfect score by just doing nothing and pass all other competitors that had done an amazing hard work to get a good score."},{"metadata":{},"cell_type":"markdown","source":"If you also think that this is incredible unfair to our community, please upvote this kernel."}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}