{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Blending some of the top Kernels"},{"metadata":{},"cell_type":"markdown","source":"More Blending inspired from Giba's Kernel : [https://www.kaggle.com/titericz/blend-or-not-to-blend-that-is-the-question].\n\nother used models:=\n1. https://www.kaggle.com/scaomath/no-memory-reduction-workflow-for-each-type-lb-1-28\n2. https://www.kaggle.com/lpachuong/statstack\n3. https://www.kaggle.com/vicensgaitan/giba-r-data-table-simplefeat-cyv-interaction\n4. https://www.kaggle.com/todnewman/keras-neural-net-for-champs\n\nThis blend is to show blending diffrent models make a huge diffrences. \n\n"},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"cell_type":"code","source":"#loading packages\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nprint(os.listdir(\"../input\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#reading all submission files\nsub1 = pd.read_csv('../input/blender/LGB_2019-07-11_-1.4378.csv')\nsub2 = pd.read_csv('../input/blender/submission-2.csv')\nsub3 = pd.read_csv('../input/blend3/stack_median.csv')\nsub4 = pd.read_csv('../input/blender2/submission.csv')\nsub5 = pd.read_csv('../input/blender2/submission-giba-1.csv')\nsub6 = pd.read_csv('../input/blender/workingsubmission-test.csv')\nprint( sub1['scalar_coupling_constant'].describe() )\nprint( sub2['scalar_coupling_constant'].describe() )\nprint( sub3['scalar_coupling_constant'].describe() )\nprint( sub4['scalar_coupling_constant'].describe() )\nprint( sub5['scalar_coupling_constant'].describe() )\nprint( sub6['scalar_coupling_constant'].describe() )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Random weights to each submission by trying and experimenting\nsub1['scalar_coupling_constant'] = 0.30*sub1['scalar_coupling_constant'] + 0.20*sub2['scalar_coupling_constant'] + 0.30*sub3['scalar_coupling_constant'] + 0.10*sub4['scalar_coupling_constant'] + 0.10*sub5['scalar_coupling_constant'] \nsub1.to_csv('submission1236.csv', index=False )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plotting histogram\nsub1['scalar_coupling_constant'].plot('hist', bins=100)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.6"}},"nbformat":4,"nbformat_minor":1}