{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"competition","sourceId":118765,"databundleVersionId":15231210,"isSourceIdPinned":false}],"dockerImageVersionId":31286,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## このNotebookの対象\n\n・このコンペに初参加の人  \n・RNA folding を初めて触る人  \n・まず提出までやりたい人","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","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 1. コンペ概要\n\nこのコンペでは **RNA配列から3D構造を予測**します。\n\n入力\nRNA sequence\n\n出力\n各塩基の3D座標\n\nつまり\n\nRNA配列 → 3D座標\n\nという問題です。\n\nこのNotebookでは\n\n・データ構造を理解する  \n・提出形式を確認する  \n・最低限の提出を作る  \n\nことを目的にします。","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport os","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-09T11:13:42.765392Z","iopub.execute_input":"2026-03-09T11:13:42.765832Z","iopub.status.idle":"2026-03-09T11:13:43.180945Z","shell.execute_reply.started":"2026-03-09T11:13:42.765801Z","shell.execute_reply":"2026-03-09T11:13:43.179604Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 2 データ構造を確認\n\nまずデータを見てみます。","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"DATA_PATH = \"/kaggle/input/competitions/stanford-rna-3d-folding-2\"\n\nprint(os.listdir(DATA_PATH))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-09T11:15:00.443101Z","iopub.execute_input":"2026-03-09T11:15:00.443454Z","iopub.status.idle":"2026-03-09T11:15:00.449591Z","shell.execute_reply.started":"2026-03-09T11:15:00.44339Z","shell.execute_reply":"2026-03-09T11:15:00.448772Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train = pd.read_csv(f\"{DATA_PATH}/train_sequences.csv\")\n\ntrain.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-09T11:15:07.965672Z","iopub.execute_input":"2026-03-09T11:15:07.965994Z","iopub.status.idle":"2026-03-09T11:15:08.799938Z","shell.execute_reply.started":"2026-03-09T11:15:07.965964Z","shell.execute_reply":"2026-03-09T11:15:08.799199Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"各行はRNA配列を表しています。\n\n主な列\n\nsequence  \nRNAの配列\n\nid  \nRNAのID","metadata":{}},{"cell_type":"markdown","source":"## RNA配列の長さ","metadata":{}},{"cell_type":"code","source":"train[\"length\"] = train[\"sequence\"].str.len()\n\ntrain[\"length\"].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-09T11:15:24.271905Z","iopub.execute_input":"2026-03-09T11:15:24.272358Z","iopub.status.idle":"2026-03-09T11:15:24.294236Z","shell.execute_reply.started":"2026-03-09T11:15:24.27232Z","shell.execute_reply":"2026-03-09T11:15:24.293009Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"RNA配列は\n\nA\nU\nC\nG\n\nの4文字で構成されています。","metadata":{}},{"cell_type":"markdown","source":"## 提出ファイルの確認","metadata":{}},{"cell_type":"code","source":"sample = pd.read_csv(f\"{DATA_PATH}/sample_submission.csv\")\n\nsample.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-09T11:15:49.97332Z","iopub.execute_input":"2026-03-09T11:15:49.974297Z","iopub.status.idle":"2026-03-09T11:15:50.014393Z","shell.execute_reply.started":"2026-03-09T11:15:49.974261Z","shell.execute_reply":"2026-03-09T11:15:50.012977Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"submissionでは\n\nid\nx\ny\nz\n\nの座標を提出します。","metadata":{}},{"cell_type":"markdown","source":"## 超シンプルベースライン\n\n今回は\n\nすべての座標を0にする\n\nというダミーモデルを作ります。\n\nもちろん精度は低いですが  \n提出形式を理解することが目的です。","metadata":{}},{"cell_type":"code","source":"sub = sample.copy()\n\nsub[\"x\"] = 0\nsub[\"y\"] = 0\nsub[\"z\"] = 0\n\nsub.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-09T11:16:11.019767Z","iopub.execute_input":"2026-03-09T11:16:11.020146Z","iopub.status.idle":"2026-03-09T11:16:11.04925Z","shell.execute_reply.started":"2026-03-09T11:16:11.020116Z","shell.execute_reply":"2026-03-09T11:16:11.048465Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sub.to_csv(\"submission.csv\",index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-09T11:16:16.134849Z","iopub.execute_input":"2026-03-09T11:16:16.135192Z","iopub.status.idle":"2026-03-09T11:16:16.194213Z","shell.execute_reply.started":"2026-03-09T11:16:16.135163Z","shell.execute_reply":"2026-03-09T11:16:16.193176Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## まとめ\n\nこのNotebookでは\n\n・RNA配列データの確認  \n・配列長の確認  \n・submission形式の確認  \n・最低限の提出作成  \n\nを行いました。\n\nこのコンペは\n\nRNA構造予測\n\nという非常に難しい問題です。\n\nそのため\n\nまずデータ構造を理解することが重要です。\n\n今後の改善案\n\n・既存のRNA foldingモデルを利用  \n・Transformerモデル  \n・Graph Neural Network  \n・AlphaFold系手法","metadata":{}}]}