{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"collapsed":true},"cell_type":"code","source":"\n\n# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\n\nimport os\n\nprint(os.listdir(\"../input\"))\n\n\n# Any results you write to the current directory are saved as output.\n\nimport numpy as np\n\nimport pandas as pd\n\nimport math\n\nimport time\n\nimport os.path\n\nd1=pd.read_csv('../input/modele1/blend 06.csv')\n\nd2=pd.read_csv('../input/modele1/lgsub.csv')\n\n\nd4=pd.read_csv('../input/modele1/lgsub.csv')\nd3=pd.read_csv('../input/aaaabbb/Add_new_5.csv')\n\nd5=pd.read_csv('../input/modele1/lgsub.csv')\n\nd4['deal_probability']=(d1['deal_probability']+d2['deal_probability'])/2\n\nd5['deal_probability']=(d4['deal_probability']+d3['deal_probability'])/2\nd5['deal_probability']=(d4['deal_probability']+d5['deal_probability'])/2\n#d2['deal_probability']=(d1['deal_probability']+d2['deal_probability'])/2\n\n#d1['deal_probability']=(d1['deal_probability']+d2['deal_probability'])/2\n\n#d2['deal_probability']=(d1['deal_probability']+d2['deal_probability'])/2\n\n#d1['deal_probability']=(d1['deal_probability']+d2['deal_probability'])/2\n\n#d2['deal_probability']=(d1['deal_probability']+d2['deal_probability'])/2\n\n#d2.to_csv(\"submission.csv\", index=False)\n\n","execution_count":1,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true,"collapsed":true},"cell_type":"code","source":"def rmse(y, y0):\n\n    assert len(y) == len(y0)\n\n    return np.sqrt(np.mean(np.power((y - y0), 2)))\n\n\nprint(\"rmse with d1\")\n\nprint (rmse(d1['deal_probability'], d2['deal_probability']),\n\nrmse(d1['deal_probability'], d3['deal_probability']),\n\nrmse(d1['deal_probability'], d4['deal_probability']),\n\nrmse(d1['deal_probability'], d5['deal_probability']))\n\n\n\nprint(\"rmse with d2\")\n\nprint(rmse(d2['deal_probability'], d3['deal_probability']),\n\nrmse(d2['deal_probability'], d4['deal_probability']),\n\nrmse(d2['deal_probability'], d5['deal_probability']),)\nprint(\"rmse with d3\")\nprint (rmse(d3['deal_probability'], d4['deal_probability']))\nrmse(d3['deal_probability'], d5['deal_probability'])\nprint(\"rmse with d4\")\nprint (rmse(d4['deal_probability'], d5['deal_probability']))\n#d1 and d3\nd3['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd1['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd3['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd1['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd3['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd1['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd3['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd3['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd1['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd3['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd1['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd3['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd1['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\nd3['deal_probability']=(d1['deal_probability']+d3['deal_probability'])/2\n#d2 and d4\nd2['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd4['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd4['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd4['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd4['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd4['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd4['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d4['deal_probability']+d2['deal_probability'])/2\n#d2 and d3\nd2['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd3['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd3['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd3['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd3['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd3['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd3['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d3['deal_probability']+d2['deal_probability'])/2\nrmse(d2['deal_probability'], d3['deal_probability'])\n#d2 and d5\nrmse(d2['deal_probability'], d5['deal_probability'])\nd2['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd5['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd5['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd5['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd5['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd5['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd5['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nd2['deal_probability']=(d5['deal_probability']+d2['deal_probability'])/2\nrmse(d2['deal_probability'], d5['deal_probability'])\nonce = d2.copy()\nonce2 = once.copy()\nd1=pd.read_csv('../input/modele1/blend 06.csv')\n\nd2=pd.read_csv('../input/modele1/lgsub.csv')\n\n\nd4=pd.read_csv('../input/modele1/lgsub.csv')\nd3=pd.read_csv('../input/aaaabbb/Add_new_5.csv')\n\nd5=pd.read_csv('../input/modele1/lgsub.csv')\n\nd4['deal_probability']=(d1['deal_probability']+d2['deal_probability'])/2\n\nd5['deal_probability']=(d4['deal_probability']+d3['deal_probability'])/2\nd5['deal_probability']=(d4['deal_probability']+d5['deal_probability'])/2\n\nprint (rmse(once['deal_probability'], d1['deal_probability']),\nrmse(once['deal_probability'], d2['deal_probability']),\nrmse(once['deal_probability'], d3['deal_probability']),\nrmse(once['deal_probability'], d4['deal_probability']),\nrmse(once['deal_probability'], d5['deal_probability']))\nd5['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nonce['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nd5['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nonce['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nd5['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nonce['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nd5['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nonce['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nd5['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nonce['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nd5['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nonce['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nd5['deal_probability']=(once['deal_probability']+d5['deal_probability'])/2\nrmse(d5['deal_probability'], once['deal_probability'])\nd5.to_csv(\"subb.csv\", index=False)\n\n","execution_count":3,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6de92f1b5e3c73f4d4776be023e14b982f1afa73","collapsed":true},"cell_type":"code","source":"","execution_count":31,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"a37f1ed57dee9ab8c35618576c61c03a97611155"},"cell_type":"code","source":"","execution_count":4,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"187f70bf42548fc46f104f9e8ad35ad0c014fa44","collapsed":true},"cell_type":"code","source":"","execution_count":5,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"2a20fb95854d29bda60737a10898fe5ad70b63d8"},"cell_type":"code","source":"","execution_count":6,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"92ddbdc16c6c3aefdc785bdc879ccbb9debaf395","collapsed":true},"cell_type":"code","source":"","execution_count":7,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"2e130af9b90614a7875e97ba70598a75f705f854"},"cell_type":"code","source":"","execution_count":8,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"130018557b902b313d4a0204f695795e3b7881f7"},"cell_type":"code","source":"","execution_count":9,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"22ff46478895ea87296514c7dc485ee9f315c55e"},"cell_type":"code","source":"","execution_count":10,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"501fd5659c3f3f80468fbc6af8aa1991672c44f4"},"cell_type":"code","source":"","execution_count":11,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"6209f7b2fc35ab997d84208ad5dc03f4c390947f"},"cell_type":"code","source":"","execution_count":12,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"da29f4a344b8c653c2acb00de0c19f3a83b7bc67"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"4df0538c9cc3f89fbf89938aad9a068b91659dfb","collapsed":true},"cell_type":"code","source":"","execution_count":32,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0918a865f49dbf24a4465227f0b1e888a0d71011","collapsed":true},"cell_type":"code","source":"","execution_count":33,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"de95f3418e54527e9a8ecdbfb774ab43432f9419"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"bf1aca94e73aa14d8aeaf0aef2ca587062363e34"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"a4f85747a344b92c7f4febc178dd2c55824fc142"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"86c3e2cb15526f75476e92d2cf1525d0b9d7fe1b"},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.5","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}