{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":116062,"databundleVersionId":14875579,"sourceType":"competition"},{"sourceId":14483971,"sourceType":"datasetVersion","datasetId":9251134},{"sourceId":14493549,"sourceType":"datasetVersion","datasetId":9257019},{"sourceId":14503500,"sourceType":"datasetVersion","datasetId":9263259}],"dockerImageVersionId":31239,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"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,"execution":{"iopub.status.busy":"2026-01-15T05:47:32.834569Z","iopub.execute_input":"2026-01-15T05:47:32.834973Z","iopub.status.idle":"2026-01-15T05:47:32.852504Z","shell.execute_reply.started":"2026-01-15T05:47:32.834945Z","shell.execute_reply":"2026-01-15T05:47:32.851575Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nprint(os.listdir(\"/kaggle/working\"))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-15T05:47:36.433785Z","iopub.execute_input":"2026-01-15T05:47:36.435291Z","iopub.status.idle":"2026-01-15T05:47:36.440534Z","shell.execute_reply.started":"2026-01-15T05:47:36.435255Z","shell.execute_reply":"2026-01-15T05:47:36.439653Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"SIM_PATH = \"/kaggle/input/similaritysubmission/submission (3).tsv\"\nLR_PATH  = \"/kaggle/input/submissionfile/submission (1).tsv\"\nOUT_PATH = \"/kaggle/working/submission.tsv\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-15T05:48:30.325024Z","iopub.execute_input":"2026-01-15T05:48:30.325321Z","iopub.status.idle":"2026-01-15T05:48:30.331196Z","shell.execute_reply.started":"2026-01-15T05:48:30.325301Z","shell.execute_reply":"2026-01-15T05:48:30.330139Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from collections import defaultdict\nfrom tqdm import tqdm\n\nSIM_WEIGHT = 0.7\nLR_WEIGHT  = 0.3\n\nensemble_scores = defaultdict(float)\n\nprint(\"Loading similarity predictions...\")\nwith open(SIM_PATH) as f:\n    for line in tqdm(f):\n        pid, go, score = line.strip().split(\"\\t\")\n        ensemble_scores[(pid, go)] += SIM_WEIGHT * float(score)\n\nprint(\"Loading LR predictions...\")\nwith open(LR_PATH) as f:\n    for line in tqdm(f):\n        pid, go, score = line.strip().split(\"\\t\")\n        ensemble_scores[(pid, go)] += LR_WEIGHT * float(score)\n\nprint(\"Writing ensemble submission...\")\nwith open(OUT_PATH, \"w\") as fout:\n    for (pid, go), score in tqdm(ensemble_scores.items()):\n        if score > 0:\n            fout.write(f\"{pid}\\t{go}\\t{round(score,3)}\\n\")\n\nprint(\"Saved:\", OUT_PATH)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-15T05:48:34.519809Z","iopub.execute_input":"2026-01-15T05:48:34.520159Z","iopub.status.idle":"2026-01-15T05:51:48.712846Z","shell.execute_reply.started":"2026-01-15T05:48:34.520133Z","shell.execute_reply":"2026-01-15T05:51:48.711843Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}