{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-output":false},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"2f204a8e0330f691feeb49843833f3770bbad23a"},"cell_type":"markdown","source":"# Load best score submission files from public kernels"},{"metadata":{"trusted":true,"_uuid":"6f322862b0e993099a71ff02e33dbbc54b70ee76"},"cell_type":"code","source":"# https://www.kaggle.com/meaninglesslives/simple-neural-net-for-time-series-classification\ndf_1375 = pd.read_csv(\"../input/submissions-plasticc/single_predictions_1.375.csv\")\n\n# https://www.kaggle.com/ogrellier/plasticc-in-a-kernel-meta-and-data\ndf_1425 = pd.read_csv(\"../input/submissions-plasticc/single_predictions_1.425.csv\")\n\n# https://www.kaggle.com/mithrillion/know-your-objective\ndf_1431 = pd.read_csv(\"../input/submissions-plasticc/single_predictions_1.431.csv\")\n\n# https://www.kaggle.com/ashishpatel26/can-this-make-sense-of-the-universe-tuned\ndf_1685 = pd.read_csv(\"../input/submissions-plasticc/single_predictions_1.685.csv\")\n\n# https://www.kaggle.com/meaninglesslives/lgb-parameter-tuning\ndf_1686 = pd.read_csv(\"../input/submissions-plasticc/single_predictions_1.686.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"55d72cf932012c2429784701562b63226a0a2f60"},"cell_type":"code","source":"# coefs\ncoefs = [0.5, 0.25, 0.25]\n\ndf_blend = df_1375 * coefs[0] + df_1425 * coefs[1] + df_1431 * coefs[2]\ndf_blend['object_id'] = df_1375 ['object_id']\n\nprint(df_blend.shape)\ndf_blend.head()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"14175e17f21533f808a164dfe892a8e9f0c861d9"},"cell_type":"markdown","source":"# save submission"},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true,"_kg_hide-output":false},"cell_type":"code","source":"df_blend.to_csv('blend_submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"c771e5cec72264f300fe012c8317be72d4405ab1"},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}