{"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_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"\nAnalysis of blends streamlining excellent notebook:\nhttps://www.kaggle.com/code/mehrankazeminia/3-op2-feature-augmentation-lightgbm/notebook\n\nResults:\n\n    Version 6: 0.592 - 0.720 scored - removed, three others weighted 1/3\n    Version 5: 0.587 - 0.720 scored - weight lowered to 0.1, others equal\n    Version 4: 0.586 - 0.720 scored - weight lowered to 0.2, others equal\n    Version 3: 0.588 - just average main 4 solutions\n    \n    Version 2: 0.664 - original prediction from the original notebook ","metadata":{}},{"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\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 read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-11-05T19:49:40.820383Z","iopub.execute_input":"2023-11-05T19:49:40.821022Z","iopub.status.idle":"2023-11-05T19:49:41.346099Z","shell.execute_reply.started":"2023-11-05T19:49:40.820987Z","shell.execute_reply":"2023-11-05T19:49:41.345207Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"s720 = pd.read_csv('/kaggle/input/op2-720/op2_720.csv', index_col='id')\ns607 = pd.read_csv('/kaggle/input/op2-607/OP2_607.csv', index_col='id')\ns604 = pd.read_csv('/kaggle/input/op2-604/submission_preds/kaggle/working/submission_df.csv', index_col='id')\ns603 = pd.read_csv('/kaggle/input/op2-603/op2_603.csv', index_col='id')\n\ns617_Priors = pd.read_csv('/kaggle/input/open-problems-2-submits-collection/LB617_Priors0802noModel_Lonnie_nbV6.csv' , index_col='id')\n\ns635_PytorchEmbed = pd.read_csv('/kaggle/input/open-problems-2-submits-collection/LB635_PytorchEmbeds_Alex_nbV1.csv', index_col='id')\n\ns621_Conv1D = pd.read_csv('/kaggle/input/open-problems-2-submits-collection/LB621_Conv1D_Lonnie_nbV24.csv' , index_col='id')\n\ns633_NN_Kibira = pd.read_csv('/kaggle/input/open-problems-2-submits-collection/LB633_NN_Kibira_nbV1.csv' , index_col='id')\n\n# 0.588: blend = (s603+s720+s604+s607)/4\n# 0.586 blend = (0.8/3*s603+0.2*s720+0.8/3*s604+0.8/3*s607)\n# 0.587 blend = (0.3*s603+0.1*s720+0.3*s604+0.3*s607)\n# 0.592 blend = (1.0/3*s603+0.0*s720+1.0/3*s604+1.0/3*s607)\n\n# See https://www.kaggle.com/competitions/open-problems-single-cell-perturbations/discussion/453110#2513189\n\n# 0.587 blend = (0.8/3*s603+0.2*s720+0.8/3*s604+0.8/3*s607)*0.8+ 0.2*s617_Priors\n\n# 0.587 blend = (0.8/3*s603+0.2*s720+0.8/3*s604+0.8/3*s607)*0.85+ 0.15*s635_PytorchEmbed\n\n# 0.587 blend = (0.8/3*s603+0.2*s720+0.8/3*s604+0.8/3*s607)*0.9+ 0.1*s621_Conv1D\n\n# 0.587 blend = (0.8/3*s603+0.2*s720+0.8/3*s604+0.8/3*s607)*0.9+ 0.1/3*(s621_Conv1D + s635_PytorchEmbed + s633_NN_Kibira )\n\n\n\nblend.to_csv('submission.csv')","metadata":{"execution":{"iopub.status.busy":"2023-11-04T18:37:30.412084Z","iopub.execute_input":"2023-11-04T18:37:30.412612Z","iopub.status.idle":"2023-11-04T18:38:07.517505Z","shell.execute_reply.started":"2023-11-04T18:37:30.412576Z","shell.execute_reply":"2023-11-04T18:38:07.516416Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# !cp /kaggle/input/3-op2-feature-augmentation-lightgbm/prediction.csv submit_featureaugmentationlightgbm_from_prediction.csv","metadata":{"execution":{"iopub.status.busy":"2023-11-04T18:38:07.518944Z","iopub.execute_input":"2023-11-04T18:38:07.519281Z","iopub.status.idle":"2023-11-04T18:38:07.524678Z","shell.execute_reply.started":"2023-11-04T18:38:07.519253Z","shell.execute_reply":"2023-11-04T18:38:07.523331Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}