{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":96164,"databundleVersionId":11418275,"sourceType":"competition"},{"sourceId":12222425,"sourceType":"datasetVersion","datasetId":7610337},{"sourceId":12241846,"sourceType":"datasetVersion","datasetId":7713303},{"sourceId":12242048,"sourceType":"datasetVersion","datasetId":7713513},{"sourceId":12242235,"sourceType":"datasetVersion","datasetId":7713520}],"dockerImageVersionId":31040,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"\n#### Dear developers\n\n  \n\nThe technology of assembling '**tida**' was applied, on the first attempt it showed the result slightly higher than the top solution, the results of four public solutions were used, as in the [previous](https://www.kaggle.com/code/nina2025/drw-crypto/) work.\n\nIn the near future its algorithm will be published.\n\n  \n\n  **Top 4**  public notebooks - 21.06.2025 - LB = 0.12_203\n\n- 0.12092 - v.01 - [DRW Competition | End-to-End Notebook](https://www.kaggle.com/code/battleninza/drw-competition-end-to-end-notebook/notebook) - [ABHAY SINGH](https://www.kaggle.com/battleninza)\n- 0.11914 - v.06 - [DRW - Crypto d75cea](https://www.kaggle.com/code/jasonzhang2022/drw-crypto-d75cea/notebook) - [jason](https://www.kaggle.com/jasonzhang2022)\n- 0.11893 - v.03 - [DRW_2](https://www.kaggle.com/code/rozzzz/drw-2/output?scriptVersionId=245591154) - [ROZZZZ](https://www.kaggle.com/rozzzz)\n- 0.11756 - v.14 - [DRW Competition Submission](https://www.kaggle.com/code/rosswade/drw-competition-submission/notebook?scriptVersionId=245352571) - [Ross Wade](https://www.kaggle.com/rosswade)","metadata":{}},{"cell_type":"markdown","source":"#### First glance","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\n\npath = '/kaggle/input/drw-quatro-3/submission__'\n\ndf = pd.read_csv(path + 'first_glance_wts(74 13 08 05) 50x50.csv')\n\ndf.to_csv(\"submission__0_12_203.csv\", index=False)  # Lb=0.12_203\n\ndf","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-22T00:32:31.871856Z","iopub.execute_input":"2025-06-22T00:32:31.873605Z","iopub.status.idle":"2025-06-22T00:32:33.392521Z","shell.execute_reply.started":"2025-06-22T00:32:31.873563Z","shell.execute_reply":"2025-06-22T00:32:33.391432Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### Second glance","metadata":{}},{"cell_type":"code","source":" %run /kaggle/input/drw-quatro-tida/_Tida.py","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-22T01:07:15.486314Z","iopub.execute_input":"2025-06-22T01:07:15.487028Z","iopub.status.idle":"2025-06-22T01:07:15.501669Z","shell.execute_reply.started":"2025-06-22T01:07:15.487003Z","shell.execute_reply":"2025-06-22T01:07:15.500559Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"path = '/kaggle/input/drw-quatro-4/submission__'\n                                                     # wts=[74 13 08 05] 50x50  # Lb=0.12_203\nFiN = ['0.12_092','0.11_914','0.11_893','0.11_756']  # wts=[77 11 07 05] 50x50  # Lb=0.12_196\nFiN = ['0.12_092','0.11_914','0.11_893','0.11_756']  # wts=[70 13 10 07] 50x50  # Lb=0.12_206\nFiN = ['0.12_092','0.11_914','0.11_893','0.11_756']  # wts=[70 12 11 07] 50x50  # Lb=0.12_206\n\ndf = _Tida ( path, FiN )\n\ndf.to_csv('submission.csv', index=False)  # Lb=?\n\ndisplay(df)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-22T01:08:37.256606Z","iopub.execute_input":"2025-06-22T01:08:37.257715Z","iopub.status.idle":"2025-06-22T01:09:21.036430Z","shell.execute_reply.started":"2025-06-22T01:08:37.257668Z","shell.execute_reply":"2025-06-22T01:09:21.035380Z"}},"outputs":[],"execution_count":null}]}