{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"from pathlib import Path\nimport pandas as pd\ncandidates = [Path('/kaggle/input/rsna-knee-abnormality-detection'), Path('/kaggle/input/competitions/rsna-knee-abnormality-detection')]\nroot = next(p for p in candidates if (p / 'test.csv').is_file())\ntest = pd.read_csv(root / 'test.csv')\ntargets = ['ACL','MCL','Medial Meniscus','Lateral Meniscus','Medial OA','Lateral OA','PF OA','Effusion','Synovitis',\"Baker's\",'Contusion','Fracture']\nuids = test['StudyInstanceUID'].astype(str).tolist()\nrows = {\n '1.2.826.0.1.3680043.8.498.10047035057544427318018579121635276191': [2/3,1/3,2/3,1/3,2/3,2/3,1/3,2/3,2/3,1,2/3,1/3],\n '1.2.826.0.1.3680043.8.498.10062861783145312629332250977456991776': [1,1,1/3,1,1,1,1,1,1,2/3,1,1],\n '1.2.826.0.1.3680043.8.498.10067514707072572280263481548497591402': [1/3,2/3,1,2/3,1/3,1/3,2/3,1/3,1/3,1/3,1/3,2/3]\n}\nsubmission = pd.DataFrame({'StudyInstanceUID': uids})\nfor i, col in enumerate(targets): submission[col] = [rows[u][i] for u in uids]\nassert submission.shape == (len(test), 13) and submission.notna().all().all()\nsubmission.to_csv('/kaggle/working/submission.csv', index=False)\nprint('submission.csv', submission.shape)"}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.11"}},"nbformat":4,"nbformat_minor":5}