{"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":"nvidiaTeslaT4","dataSources":[{"sourceId":99552,"databundleVersionId":13851420,"sourceType":"competition"}],"dockerImageVersionId":31153,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport glob\nimport numpy as np\nimport pandas as pd\nimport pydicom\nfrom PIL import Image\nimport SimpleITK as sitk\nfrom concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, as_completed\nfrom tqdm import tqdm\nimport shutil\nimport gc\n\n# Paths and Config\nROOT_DICOM = \"/kaggle/input/rsna-intracranial-aneurysm-detection/series\"\ntrain_df=\"/kaggle/input/rsna-intracranial-aneurysm-detection/train.csv\"\n#OUTPUT_PNG_ROOT = \"/kaggle/working/converted_png_224x224\" # temp directory for resized PNGs\nROOT_MASK = \"/kaggle/input/rsna-intracranial-aneurysm-detection/segmentations\"\n\nOUTPUT_PNG_ROOT = \"/kaggle/tmp/converted_png_224x224\"\nOUTPUT_DIR = \"/kaggle/tmp/resampled_output\"\n\nTARGET_SIZE = (224, 224)\nTARGET_SPACING = (1.0, 1.0, 1.0)\nos.makedirs(OUTPUT_PNG_ROOT, exist_ok=True)\nos.makedirs(OUTPUT_DIR, exist_ok=True)\n\n# Load series to modality mapping CSV\nmetadata_csv = \"/kaggle/input/rsna-intracranial-aneurysm-detection/train.csv\"\ndf = pd.read_csv(metadata_csv)\nseries_to_modality = dict(zip(df['SeriesInstanceUID'], df['Modality']))\nmodalities_to_include = [\"CTA\", \"MRA\", \"MRI T1post\", \"MRI T2\"]\ndf.head()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-10-13T12:50:09.038497Z","iopub.execute_input":"2025-10-13T12:50:09.038721Z","iopub.status.idle":"2025-10-13T12:50:11.96434Z","shell.execute_reply.started":"2025-10-13T12:50:09.038698Z","shell.execute_reply":"2025-10-13T12:50:11.963465Z"}},"outputs":[{"execution_count":1,"output_type":"execute_result","data":{"text/plain":" SeriesInstanceUID PatientAge PatientSex \\\n0 1.2.826.0.1.3680043.8.498.10004044428023505108... 64 Female \n1 1.2.826.0.1.3680043.8.498.10004684224894397679... 76 Female \n2 1.2.826.0.1.3680043.8.498.10005158603912009425... 58 Male \n3 1.2.826.0.1.3680043.8.498.10009383108068795488... 71 Male \n4 1.2.826.0.1.3680043.8.498.10012790035410518400... 48 Female \n\n Modality Left Infraclinoid Internal Carotid Artery \\\n0 MRA 0 \n1 MRA 0 \n2 CTA 0 \n3 MRA 0 \n4 MRA 0 \n\n Right Infraclinoid Internal Carotid Artery \\\n0 0 \n1 0 \n2 0 \n3 0 \n4 0 \n\n Left Supraclinoid Internal Carotid Artery \\\n0 0 \n1 0 \n2 0 \n3 0 \n4 0 \n\n Right Supraclinoid Internal Carotid Artery Left Middle Cerebral Artery \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n\n Right Middle Cerebral Artery Anterior Communicating Artery \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n\n Left Anterior Cerebral Artery Right Anterior Cerebral Artery \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n\n Left Posterior Communicating Artery Right Posterior Communicating Artery \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n\n Basilar Tip Other Posterior Circulation Aneurysm Present \n0 0 0 0 \n1 0 0 0 \n2 0 1 1 \n3 0 0 0 \n4 0 0 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SeriesInstanceUIDPatientAgePatientSexModalityLeft Infraclinoid Internal Carotid ArteryRight Infraclinoid Internal Carotid ArteryLeft Supraclinoid Internal Carotid ArteryRight Supraclinoid Internal Carotid ArteryLeft Middle Cerebral ArteryRight Middle Cerebral ArteryAnterior Communicating ArteryLeft Anterior Cerebral ArteryRight Anterior Cerebral ArteryLeft Posterior Communicating ArteryRight Posterior Communicating ArteryBasilar TipOther Posterior CirculationAneurysm Present
01.2.826.0.1.3680043.8.498.10004044428023505108...64FemaleMRA00000000000000
11.2.826.0.1.3680043.8.498.10004684224894397679...76FemaleMRA00000000000000
21.2.826.0.1.3680043.8.498.10005158603912009425...58MaleCTA00000000000011
31.2.826.0.1.3680043.8.498.10009383108068795488...71MaleMRA00000000000000
41.2.826.0.1.3680043.8.498.10012790035410518400...48FemaleMRA00000000000000
\n
"},"metadata":{}}],"execution_count":1},{"cell_type":"code","source":"!pip install timm\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T12:50:11.966167Z","iopub.execute_input":"2025-10-13T12:50:11.966398Z","iopub.status.idle":"2025-10-13T12:51:25.01111Z","shell.execute_reply.started":"2025-10-13T12:50:11.96638Z","shell.execute_reply":"2025-10-13T12:51:25.010342Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: timm in /usr/local/lib/python3.11/dist-packages (1.0.19)\nRequirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (from timm) (2.6.0+cu124)\nRequirement already satisfied: torchvision in /usr/local/lib/python3.11/dist-packages (from timm) (0.21.0+cu124)\nRequirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from timm) (6.0.3)\nRequirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/dist-packages (from timm) (1.0.0rc2)\nRequirement already satisfied: safetensors in /usr/local/lib/python3.11/dist-packages (from timm) (0.5.3)\nRequirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface_hub->timm) (3.19.1)\nRequirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub->timm) (2025.9.0)\nRequirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub->timm) (25.0)\nRequirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub->timm) (0.28.1)\nRequirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub->timm) (4.67.1)\nRequirement already satisfied: typer-slim in /usr/local/lib/python3.11/dist-packages (from huggingface_hub->timm) (0.19.2)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub->timm) (4.15.0)\nRequirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub->timm) (1.1.10)\nRequirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch->timm) (3.5)\nRequirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch->timm) (3.1.6)\nCollecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch->timm)\n Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\nCollecting nvidia-cuda-runtime-cu12==12.4.127 (from torch->timm)\n Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\nCollecting nvidia-cuda-cupti-cu12==12.4.127 (from torch->timm)\n Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\nCollecting nvidia-cudnn-cu12==9.1.0.70 (from torch->timm)\n Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\nCollecting nvidia-cublas-cu12==12.4.5.8 (from torch->timm)\n Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\nCollecting nvidia-cufft-cu12==11.2.1.3 (from torch->timm)\n Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\nCollecting nvidia-curand-cu12==10.3.5.147 (from torch->timm)\n Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\nCollecting nvidia-cusolver-cu12==11.6.1.9 (from torch->timm)\n Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\nCollecting nvidia-cusparse-cu12==12.3.1.170 (from torch->timm)\n Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\nRequirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch->timm) (0.6.2)\nRequirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch->timm) (2.21.5)\nRequirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch->timm) (12.4.127)\nCollecting nvidia-nvjitlink-cu12==12.4.127 (from torch->timm)\n Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\nRequirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch->timm) (3.2.0)\nRequirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch->timm) (1.13.1)\nRequirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch->timm) (1.3.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from torchvision->timm) (1.26.4)\nRequirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.11/dist-packages (from torchvision->timm) (11.3.0)\nRequirement already satisfied: anyio in /usr/local/lib/python3.11/dist-packages (from httpx<1,>=0.23.0->huggingface_hub->timm) (4.11.0)\nRequirement already satisfied: certifi in /usr/local/lib/python3.11/dist-packages (from httpx<1,>=0.23.0->huggingface_hub->timm) (2025.8.3)\nRequirement already satisfied: httpcore==1.* in /usr/local/lib/python3.11/dist-packages (from httpx<1,>=0.23.0->huggingface_hub->timm) (1.0.9)\nRequirement already satisfied: idna in /usr/local/lib/python3.11/dist-packages (from httpx<1,>=0.23.0->huggingface_hub->timm) (3.10)\nRequirement already satisfied: h11>=0.16 in /usr/local/lib/python3.11/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface_hub->timm) (0.16.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch->timm) (3.0.2)\nRequirement already satisfied: mkl_fft in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision->timm) (1.3.8)\nRequirement already satisfied: mkl_random in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision->timm) (1.2.4)\nRequirement already satisfied: mkl_umath in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision->timm) (0.1.1)\nRequirement already satisfied: mkl in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision->timm) (2025.2.0)\nRequirement already satisfied: tbb4py in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision->timm) (2022.2.0)\nRequirement already satisfied: mkl-service in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision->timm) (2.4.1)\nRequirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.11/dist-packages (from typer-slim->huggingface_hub->timm) (8.3.0)\nRequirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.11/dist-packages (from anyio->httpx<1,>=0.23.0->huggingface_hub->timm) (1.3.1)\nRequirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy->torchvision->timm) (2024.2.0)\nRequirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy->torchvision->timm) (2022.2.0)\nRequirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy->torchvision->timm) (1.4.0)\nRequirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy->torchvision->timm) (2024.2.0)\nRequirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy->torchvision->timm) (2024.2.0)\nDownloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m91.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m0:01\u001b[0m\n\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m62.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m52.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m31.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m72.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n Attempting uninstall: nvidia-nvjitlink-cu12\n Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n Attempting uninstall: nvidia-curand-cu12\n Found existing installation: nvidia-curand-cu12 10.3.6.82\n Uninstalling nvidia-curand-cu12-10.3.6.82:\n Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n Attempting uninstall: nvidia-cufft-cu12\n Found existing installation: nvidia-cufft-cu12 11.2.3.61\n Uninstalling nvidia-cufft-cu12-11.2.3.61:\n Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n Attempting uninstall: nvidia-cuda-runtime-cu12\n Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n Attempting uninstall: nvidia-cuda-nvrtc-cu12\n Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n Attempting uninstall: nvidia-cuda-cupti-cu12\n Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n Attempting uninstall: nvidia-cublas-cu12\n Found existing installation: nvidia-cublas-cu12 12.5.3.2\n Uninstalling nvidia-cublas-cu12-12.5.3.2:\n Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n Attempting uninstall: nvidia-cusparse-cu12\n Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n Attempting uninstall: nvidia-cudnn-cu12\n Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n Attempting uninstall: nvidia-cusolver-cu12\n Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nlibcugraph-cu12 25.6.0 requires libraft-cu12==25.6.*, but you have libraft-cu12 25.2.0 which is incompatible.\npylibcugraph-cu12 25.6.0 requires pylibraft-cu12==25.6.*, but you have pylibraft-cu12 25.2.0 which is incompatible.\npylibcugraph-cu12 25.6.0 requires rmm-cu12==25.6.*, but you have rmm-cu12 25.2.0 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nfrom timm.layers import DropPath\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader\nimport numpy as np\nimport pandas as pd\nimport SimpleITK as sitk\nimport os\nimport glob\nfrom tqdm import tqdm\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T12:51:25.012072Z","iopub.execute_input":"2025-10-13T12:51:25.012368Z","iopub.status.idle":"2025-10-13T12:51:35.550674Z","shell.execute_reply.started":"2025-10-13T12:51:25.012344Z","shell.execute_reply":"2025-10-13T12:51:35.55011Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/pydantic/_internal/_generate_schema.py:2225: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n warnings.warn(\n/usr/local/lib/python3.11/dist-packages/pydantic/_internal/_generate_schema.py:2225: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n warnings.warn(\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"def dicom_to_resized_png(dicom_dir, out_dir, target_size=TARGET_SIZE):\n os.makedirs(out_dir, exist_ok=True)\n dicom_files = sorted(glob.glob(os.path.join(dicom_dir, \"*.dcm\")))\n if not dicom_files:\n return False\n for i, dcm_file in enumerate(dicom_files):\n try:\n ds = pydicom.dcmread(dcm_file)\n img_array = ds.pixel_array.astype(np.float32)\n img_norm = (img_array - img_array.min()) / (img_array.max() - img_array.min() + 1e-8)\n img_uint8 = (img_norm * 255).astype(np.uint8)\n img_pil = Image.fromarray(img_uint8).convert(\"L\")\n img_resized = img_pil.resize(target_size, Image.LANCZOS)\n img_resized.save(os.path.join(out_dir, f\"{i:04d}.png\"))\n except Exception as e:\n print(f\"Failed to convert {dcm_file}: {e}\")\n return False\n return True\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T12:51:35.551487Z","iopub.execute_input":"2025-10-13T12:51:35.551746Z","iopub.status.idle":"2025-10-13T12:51:35.55763Z","shell.execute_reply.started":"2025-10-13T12:51:35.551721Z","shell.execute_reply":"2025-10-13T12:51:35.556904Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"def stack_png_series(series_dir, target_size=TARGET_SIZE):\n files = sorted(glob.glob(os.path.join(series_dir, \"*.png\")))\n slices = []\n for file in files:\n img = Image.open(file).convert(\"L\")\n img = img.resize(target_size)\n arr = np.array(img, dtype=np.float32) / 255.0\n slices.append(arr)\n if not slices:\n return None\n return np.stack(slices, axis=0)\n\ndef np_volume_to_sitk(volume, spacing):\n img = sitk.GetImageFromArray(volume.astype(np.float32))\n img.SetSpacing(spacing)\n return img\n\ndef resample_sitk_img(sitk_img, new_spacing=TARGET_SPACING, is_label=False):\n resampler = sitk.ResampleImageFilter()\n resampler.SetOutputSpacing(new_spacing)\n original_size = sitk_img.GetSize()\n original_spacing = sitk_img.GetSpacing()\n new_size = [\n int(round(osz * ospc / nspc))\n for osz, ospc, nspc in zip(original_size, original_spacing, new_spacing)\n ]\n resampler.SetSize(new_size)\n resampler.SetOutputDirection(sitk_img.GetDirection())\n resampler.SetOutputOrigin(sitk_img.GetOrigin())\n interpolator = sitk.sitkNearestNeighbor if is_label else sitk.sitkLinear\n resampler.SetInterpolator(interpolator)\n return resampler.Execute(sitk_img)\n\ndef get_dicom_spacing(dicom_dir):\n dicom_files = [f for f in os.listdir(dicom_dir) if f.endswith(\".dcm\")]\n if not dicom_files:\n raise RuntimeError(f\"No DICOM files found in {dicom_dir}\")\n for dcm_file in dicom_files:\n dcm = pydicom.dcmread(os.path.join(dicom_dir, dcm_file), stop_before_pixels=True)\n if hasattr(dcm, \"SliceThickness\") and hasattr(dcm, \"PixelSpacing\"):\n z = float(dcm.SliceThickness)\n y, x = [float(v) for v in dcm.PixelSpacing]\n return (z, y, x)\n raise RuntimeError(f\"No valid DICOM with SliceThickness and PixelSpacing found in {dicom_dir}\")\n\n\ndef register_modality(fixed_img, moving_img):\n # Histogram match for multi-modality\n matcher = sitk.HistogramMatchingImageFilter()\n matcher.SetNumberOfHistogramLevels(1024)\n matcher.SetNumberOfMatchPoints(7)\n matcher.ThresholdAtMeanIntensityOn()\n moving_img = matcher.Execute(moving_img, fixed_img)\n\n registration_method = sitk.ImageRegistrationMethod()\n registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)\n registration_method.SetOptimizerAsGradientDescent(learningRate=1.0, numberOfIterations=100)\n registration_method.SetOptimizerScalesFromPhysicalShift()\n initial_transform = sitk.CenteredTransformInitializer(\n fixed_img, moving_img, sitk.Euler3DTransform(), sitk.CenteredTransformInitializerFilter.GEOMETRY)\n registration_method.SetInitialTransform(initial_transform, inPlace=False)\n registration_method.SetInterpolator(sitk.sitkLinear)\n\n final_transform = registration_method.Execute(fixed_img, moving_img)\n moving_resampled = sitk.Resample(moving_img, fixed_img, final_transform, sitk.sitkLinear, 0.0, moving_img.GetPixelID())\n\n return moving_resampled\n\n\ndef modality_normalization(volume_np, modality):\n if modality == 'CTA':\n wl, ww = 40, 400\n min_w = wl - ww / 2\n max_w = wl + ww / 2\n volume_np = np.clip(volume_np, min_w, max_w)\n volume_np = (volume_np - min_w) / (max_w - min_w)\n elif modality in ['MRI', 'MRA', 'MRI T1post', 'MRI T2']:\n mean = volume_np.mean()\n std = volume_np.std()\n volume_np = (volume_np - mean) / (std + 1e-8)\n else:\n volume_np = (volume_np - volume_np.min()) / (volume_np.max() - volume_np.min() + 1e-8)\n return volume_np\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T12:51:35.558473Z","iopub.execute_input":"2025-10-13T12:51:35.560041Z","iopub.status.idle":"2025-10-13T12:51:35.770089Z","shell.execute_reply.started":"2025-10-13T12:51:35.560011Z","shell.execute_reply":"2025-10-13T12:51:35.769347Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"def process_case(args):\n artery, series, modality, ROOT_PNG, ROOT_MASK, ROOT_DICOM, OUTPUT_DIR, target_spacing = args\n series_dir = os.path.join(ROOT_PNG, modality, series)\n dicom_dir = os.path.join(ROOT_DICOM, series)\n out_dir_mod = os.path.join(OUTPUT_DIR, modality)\n os.makedirs(out_dir_mod, exist_ok=True)\n\n out_vol_path = os.path.join(out_dir_mod, f\"{series}_vol_resamp.nii.gz\")\n out_mask_path = os.path.join(out_dir_mod, f\"{series}_mask_resamp.nii.gz\")\n\n try:\n orig_spacing = get_dicom_spacing(dicom_dir)\n vol_np = stack_png_series(series_dir)\n if vol_np is None:\n return f\"{series} [{modality}] FAIL: No PNG slices found\"\n\n vol_sitk = np_volume_to_sitk(vol_np, spacing=orig_spacing)\n vol_resamp = resample_sitk_img(vol_sitk, new_spacing=target_spacing, is_label=False)\n\n # Modality normalization\n vol_resamp_np = sitk.GetArrayFromImage(vol_resamp)\n vol_norm_np = modality_normalization(vol_resamp_np, modality)\n vol_norm_sitk = sitk.GetImageFromArray(vol_norm_np)\n vol_norm_sitk.CopyInformation(vol_resamp)\n\n # Save normalized volume\n sitk.WriteImage(vol_norm_sitk, out_vol_path)\n\n # Process segmentation mask\n mask_path = os.path.join(ROOT_MASK, f\"{series}.nii\")\n if os.path.exists(mask_path):\n mask_sitk = sitk.ReadImage(mask_path)\n # Resample mask\n mask_resamp = resample_sitk_img(mask_sitk, new_spacing=target_spacing, is_label=True)\n\n # Optionally register mask to normalized volume (uncomment if needed)\n # mask_resamp = register_modality(vol_norm_sitk, mask_resamp)\n\n sitk.WriteImage(mask_resamp, out_mask_path)\n\n return f\"{series} [{modality}] SUCCESS\"\n except Exception as e:\n return f\"{series} [{modality}] FAIL: {e}\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T12:51:35.770887Z","iopub.execute_input":"2025-10-13T12:51:35.771162Z","iopub.status.idle":"2025-10-13T12:51:35.784211Z","shell.execute_reply.started":"2025-10-13T12:51:35.771136Z","shell.execute_reply":"2025-10-13T12:51:35.783567Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"def batch_process(series_info):\n series, modality, dicom_dir = series_info\n out_dir = os.path.join(OUTPUT_PNG_ROOT, modality, series)\n if os.path.exists(out_dir):\n shutil.rmtree(out_dir)\n success = dicom_to_resized_png(dicom_dir, out_dir, TARGET_SIZE)\n if not success:\n return f\"{series} [{modality}] Conversion failed\"\n artery = \"\" # Add artery info if available\n args = (artery, series, modality, OUTPUT_PNG_ROOT, ROOT_MASK, ROOT_DICOM, OUTPUT_DIR, TARGET_SPACING)\n result = process_case(args)\n shutil.rmtree(out_dir)\n gc.collect()\n return result\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T12:51:35.786359Z","iopub.execute_input":"2025-10-13T12:51:35.786611Z","iopub.status.idle":"2025-10-13T12:51:35.798734Z","shell.execute_reply.started":"2025-10-13T12:51:35.786597Z","shell.execute_reply":"2025-10-13T12:51:35.797991Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"series_list = [\n (series, modality, os.path.join(ROOT_DICOM, series))\n for series, modality in series_to_modality.items()\n if modality in modalities_to_include and os.path.isdir(os.path.join(ROOT_DICOM, series))\n][:1000] # Process only first 50 series for testing\n\nbatch_size = 10\nmax_workers = 5\n\nfrom tqdm import tqdm\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\n\ntotal_files = 4348 # Total number of DICOM series\n\nwith tqdm(total=total_files, desc=\"Total Resampling Progress\") as pbar:\n with ProcessPoolExecutor(max_workers=max_workers) as executor:\n futures = [executor.submit(batch_process, si) for si in series_list]\n \n # as each future completes update the main progress bar by 1\n for future in as_completed(futures):\n result = future.result()\n pbar.update(1)\n print(result)\n\n\nprint(\"✅ Full pipeline complete.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T12:51:35.799449Z","iopub.execute_input":"2025-10-13T12:51:35.799751Z","iopub.status.idle":"2025-10-13T13:43:55.274963Z","shell.execute_reply.started":"2025-10-13T12:51:35.799725Z","shell.execute_reply":"2025-10-13T13:43:55.273565Z"}},"outputs":[{"name":"stderr","text":"Total Resampling Progress: 0%| | 0/4348 [00:00= MAX_ROI:\n break\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:18:42.092236Z","iopub.execute_input":"2025-10-13T16:18:42.092983Z","iopub.status.idle":"2025-10-13T16:22:34.401548Z","shell.execute_reply.started":"2025-10-13T16:18:42.092958Z","shell.execute_reply":"2025-10-13T16:22:34.400859Z"}},"outputs":[{"name":"stdout","text":"Saved localized ROI for 1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381 at Right Anterior Cerebral Artery with center x=223, y=225\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381 at Left Middle Cerebral Artery with center x=289, y=211\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381 at Right Supraclinoid Internal Carotid Artery with center x=232, y=226\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.10076056930521523789588901704956188485 at Right Supraclinoid Internal Carotid Artery with center x=455, y=477\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.10188636688783982623025997809119805350 at Left Infraclinoid Internal Carotid Artery with center x=169, y=157\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.10410600166004340343973545138447283460 at Right Middle Cerebral Artery with center x=106, y=216\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.10540586847553109495238524904638776495 at Right Middle Cerebral Artery with center x=191, y=196\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.10929608782694347957516071062422315982 at Right Middle Cerebral Artery with center x=208, y=196\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382 at Left Posterior Communicating Artery with center x=272, y=237\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382 at Other Posterior Circulation with center x=240, y=299\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382 at Right Posterior Communicating Artery with center x=220, y=243\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11140496970152788589837488009637704168 at Left Infraclinoid Internal Carotid Artery with center x=140, y=137\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11163718560814217911019576488539324434 at Left Supraclinoid Internal Carotid Artery with center x=274, y=224\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11422928060228360802778018026859204182 at Anterior Communicating Artery with center x=247, y=220\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11447542941959800581541313722844637822 at Anterior Communicating Artery with center x=272, y=169\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11504459395565711149380261095223705023 at Right Middle Cerebral Artery with center x=194, y=219\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11557464859397815362951522785245632020 at Anterior Communicating Artery with center x=190, y=156\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11624217734793256238140178687655335066 at Right Supraclinoid Internal Carotid Artery with center x=258, y=206\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11624217734793256238140178687655335066 at Right Middle Cerebral Artery with center x=208, y=212\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11639720015527164474926997755882681707 at Other Posterior Circulation with center x=139, y=163\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11639720015527164474926997755882681707 at Other Posterior Circulation with center x=140, y=152\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11641438607169452758239778414614826230 at Other Posterior Circulation with center x=252, y=265\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11821446229980500432989393232863242415 at Left Supraclinoid Internal Carotid Artery with center x=267, y=191\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11924949819899884502738782576851659426 at Other Posterior Circulation with center x=227, y=248\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11924949819899884502738782576851659426 at Basilar Tip with center x=259, y=259\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11938739392606296532297884225608408867 at Right Middle Cerebral Artery with center x=166, y=180\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.11999987145696510072091906561590137848 at Anterior Communicating Artery with center x=345, y=273\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12132622846836853200891705613461466627 at Right Supraclinoid Internal Carotid Artery with center x=229, y=224\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12180351938456969219537687190067731477 at Other Posterior Circulation with center x=254, y=259\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12226380705607315060235896835122737788 at Basilar Tip with center x=270, y=242\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12271269630687930751200307891697907423 at Right Infraclinoid Internal Carotid Artery with center x=249, y=255\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12271269630687930751200307891697907423 at Right Infraclinoid Internal Carotid Artery with center x=254, y=259\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12427930128533148989436011949311706348 at Right Infraclinoid Internal Carotid Artery with center x=167, y=197\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12709802490782896897031103447163443069 at Right Middle Cerebral Artery with center x=171, y=218\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12773309706735630359315214846273921394 at Anterior Communicating Artery with center x=175, y=147\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12792960392435514526913217158720555996 at Right Middle Cerebral Artery with center x=87, y=157\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12812390336793304037901571645929430100 at Right Middle Cerebral Artery with center x=186, y=212\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12873050136415197430227722045995986358 at Left Middle Cerebral Artery with center x=206, y=162\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12888459003897616398890411591973176636 at Left Supraclinoid Internal Carotid Artery with center x=270, y=211\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12896910506681881306246412668919668702 at Anterior Communicating Artery with center x=234, y=202\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12896910506681881306246412668919668702 at Right Anterior Cerebral Artery with center x=224, y=244\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12896910506681881306246412668919668702 at Anterior Communicating Artery with center x=230, y=199\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12896910506681881306246412668919668702 at Right Anterior Cerebral Artery with center x=253, y=162\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12898332622076283462996059479076432725 at Left Infraclinoid Internal Carotid Artery with center x=273, y=220\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12904246053955178641505906243733756576 at Right Supraclinoid Internal Carotid Artery with center x=224, y=221\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.12914952223659958493995413641114579279 at Other Posterior Circulation with center x=155, y=212\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.13128656559176299272467358793386537400 at Right Supraclinoid Internal Carotid Artery with center x=256, y=218\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.13128656559176299272467358793386537400 at Right Supraclinoid Internal Carotid Artery with center x=241, y=205\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.13359737970612926494907045108541390310 at Right Posterior Communicating Artery with center x=224, y=203\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.13359737970612926494907045108541390310 at Anterior Communicating Artery with center x=250, y=202\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.13789305723712362238118274295587312089 at Anterior Communicating Artery with center x=249, y=200\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.14375161350968928494386548917647435597 at Left Supraclinoid Internal Carotid Artery with center x=274, y=213\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.15111820005882064793593034423469604305 at Other Posterior Circulation with center x=196, y=264\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.15412988336827906186857260013885503248 at Basilar Tip with center x=232, y=290\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.15777485274723969278718374949878560903 at Left Supraclinoid Internal Carotid Artery with center x=185, y=144\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.16386250344855221757144432829845114733 at Anterior Communicating Artery with center x=245, y=216\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.16386250344855221757144432829845114733 at Right Anterior Cerebral Artery with center x=240, y=260\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.16386250344855221757144432829845114733 at Right Anterior Cerebral Artery with center x=229, y=261\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.16386250344855221757144432829845114733 at Right Anterior Cerebral Artery with center x=266, y=180\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.16984390277144742906667579449023180512 at Left Supraclinoid Internal Carotid Artery with center x=287, y=199\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.16984390277144742906667579449023180512 at Anterior Communicating Artery with center x=256, y=196\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.17415277997649872560329721717694101082 at Other Posterior Circulation with center x=348, y=400\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.19915189891686122627071348069843885714 at Left Posterior Communicating Artery with center x=553, y=473\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.19915189891686122627071348069843885714 at Other Posterior Circulation with center x=509, y=540\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.21004106426734635526381567602936015568 at Right Anterior Cerebral Artery with center x=242, y=158\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.23047023542526806696555440426928375679 at Anterior Communicating Artery with center x=246, y=187\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.23055827202917388669053993133576833763 at Anterior Communicating Artery with center x=128, y=99\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.24023896361071846724104915533800547445 at Left Supraclinoid Internal Carotid Artery with center x=275, y=218\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.24587963869128721940158079207224095554 at Right Supraclinoid Internal Carotid Artery with center x=240, y=237\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.24941924992372724575490063788348447936 at Right Middle Cerebral Artery with center x=84, y=120\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.27693546360513068451517048347207987807 at Basilar Tip with center x=157, y=156\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.27857528510177554953207997404329765760 at Left Supraclinoid Internal Carotid Artery with center x=266, y=195\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.28722601444191262075880952461419085326 at Basilar Tip with center x=254, y=253\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.31897325247898403027455884342546675049 at Left Middle Cerebral Artery with center x=298, y=219\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.32250259987224176174516959348681094310 at Basilar Tip with center x=271, y=222\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.34439485184360273751379923196589017042 at Other Posterior Circulation with center x=159, y=157\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.35327124657045713676192746001247576881 at Right Middle Cerebral Artery with center x=189, y=183\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.35378146560080702211693278243609271022 at Anterior Communicating Artery with center x=196, y=148\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.35378146560080702211693278243609271022 at Anterior Communicating Artery with center x=201, y=147\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.36205761227502095958293403225062705137 at Right Posterior Communicating Artery with center x=209, y=240\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.36516744229109249667702200145077143886 at Left Supraclinoid Internal Carotid Artery with center x=110, y=129\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.36563348911961346172279351080943665664 at Basilar Tip with center x=269, y=310\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.36928611823925733133253145871406408988 at Anterior Communicating Artery with center x=187, y=139\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.37086262716517957668471635372810376638 at Right Middle Cerebral Artery with center x=229, y=190\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.38245669369430321272819874468980907728 at Right Posterior Communicating Artery with center x=241, y=230\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.38245669369430321272819874468980907728 at Left Posterior Communicating Artery with center x=284, y=241\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.38904475631578710113273863766282479811 at Other Posterior Circulation with center x=243, y=255\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.38904475631578710113273863766282479811 at Right Supraclinoid Internal Carotid Artery with center x=212, y=216\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.39640919070091958876744231048011388614 at Anterior Communicating Artery with center x=265, y=185\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.39640919070091958876744231048011388614 at Right Middle Cerebral Artery with center x=203, y=198\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.40402571428459178954472078378902050472 at Other Posterior Circulation with center x=245, y=260\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.42092450058597943280470345107435382425 at Basilar Tip with center x=262, y=254\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.42672154202952548010999212369080894652 at Right Supraclinoid Internal Carotid Artery with center x=240, y=199\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.42672154202952548010999212369080894652 at Other Posterior Circulation with center x=264, y=237\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.42933230680553480084056393591634621848 at Right Supraclinoid Internal Carotid Artery with center x=251, y=200\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.43495968397556043698567120038117641587 at Right Supraclinoid Internal Carotid Artery with center x=254, y=223\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.43536331102142701793144520859521601945 at Left Supraclinoid Internal Carotid Artery with center x=302, y=149\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.47622062519393262272120105951011625928 at Left Supraclinoid Internal Carotid Artery with center x=270, y=219\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.47802313478131783077762931281303667601 at Right Anterior Cerebral Artery with center x=507, y=430\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.47887093599897399482447594752785316358 at Right Supraclinoid Internal Carotid Artery with center x=241, y=206\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.49640345168968922611291772802640560828 at Basilar Tip with center x=250, y=230\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.49718418682238683779854914910561017368 at Anterior Communicating Artery with center x=249, y=203\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.50241233088534910114736887318508484246 at Anterior Communicating Artery with center x=231, y=235\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.50268462808449401128173812870329002342 at Right Middle Cerebral Artery with center x=204, y=237\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.50275403170194436966991630938339966596 at Right Supraclinoid Internal Carotid Artery with center x=234, y=203\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.50369188120242587742908379292729868174 at Right Infraclinoid Internal Carotid Artery with center x=252, y=253\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.50369188120242587742908379292729868174 at Right Infraclinoid Internal Carotid Artery with center x=252, y=258\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430 at Left Supraclinoid Internal Carotid Artery with center x=302, y=199\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430 at Right Supraclinoid Internal Carotid Artery with center x=219, y=195\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430 at Right Posterior Communicating Artery with center x=221, y=218\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.53947155422591684879953627516013605305 at Anterior Communicating Artery with center x=250, y=212\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.54865110953409154322874363435644372368 at Left Supraclinoid Internal Carotid Artery with center x=365, y=320\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.54865110953409154322874363435644372368 at Anterior Communicating Artery with center x=333, y=308\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.55051557363776453883164282380323354147 at Right Supraclinoid Internal Carotid Artery with center x=238, y=300\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.55520651046049733868642268089599441721 at Right Supraclinoid Internal Carotid Artery with center x=117, y=125\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.56109731607412273442907651635753012241 at Anterior Communicating Artery with center x=267, y=222\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.56109731607412273442907651635753012241 at Anterior Communicating Artery with center x=274, y=220\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.56479623144539472445940519727300319231 at Left Supraclinoid Internal Carotid Artery with center x=274, y=214\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.56867346585094457716984380929416039466 at Left Supraclinoid Internal Carotid Artery with center x=281, y=183\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.56867346585094457716984380929416039466 at Anterior Communicating Artery with center x=259, y=162\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.58839417089022860359638460482101293080 at Other Posterior Circulation with center x=232, y=303\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.61152918475243358118286003299125054478 at Other Posterior Circulation with center x=276, y=263\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.65011208113835286935212080363533579671 at Anterior Communicating Artery with center x=259, y=206\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.65654303333996310125136982540737772052 at Right Middle Cerebral Artery with center x=147, y=165\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.66341469849558089736451534296312923277 at Left Posterior Communicating Artery with center x=303, y=230\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.67256382079119118825371537284628604044 at Left Infraclinoid Internal Carotid Artery with center x=140, y=137\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.68161752706586485995657009830735928975 at Anterior Communicating Artery with center x=252, y=207\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.68276712082656957005274595949315894066 at Right Anterior Cerebral Artery with center x=246, y=168\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.68356160898101066850726244725552676010 at Anterior Communicating Artery with center x=252, y=190\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.68356160898101066850726244725552676010 at Basilar Tip with center x=252, y=227\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.68654901185438820364160878605611510817 at Left Supraclinoid Internal Carotid Artery with center x=319, y=320\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.68654901185438820364160878605611510817 at Anterior Communicating Artery with center x=284, y=314\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.68709340002397343932718258443293606585 at Right Supraclinoid Internal Carotid Artery with center x=216, y=242\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.69401690945645968072368812538918487252 at Anterior Communicating Artery with center x=266, y=198\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.69568746915553014138135720681936366640 at Right Middle Cerebral Artery with center x=360, y=406\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.70243202242722756546202582478829903758 at Anterior Communicating Artery with center x=262, y=192\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.71796601538792777580416179841706319140 at Right Posterior Communicating Artery with center x=217, y=229\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.72679260079421518845786364620483278827 at Right Supraclinoid Internal Carotid Artery with center x=227, y=183\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.73820261697830420042473892884688067574 at Other Posterior Circulation with center x=257, y=318\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.75016896260047968433534297207591136672 at Right Posterior Communicating Artery with center x=231, y=216\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.75294325392457179365040684378207706807 at Anterior Communicating Artery with center x=278, y=207\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.75294325392457179365040684378207706807 at Basilar Tip with center x=277, y=240\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.75798029534455454939797323020706657426 at Left Posterior Communicating Artery with center x=259, y=259\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.77257791208759842602760935296318202703 at Right Middle Cerebral Artery with center x=164, y=179\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.77257791208759842602760935296318202703 at Left Middle Cerebral Artery with center x=393, y=225\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.79099213587801933936080747802403048718 at Other Posterior Circulation with center x=231, y=238\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.79942836660118710928733936389534291771 at Left Infraclinoid Internal Carotid Artery with center x=268, y=214\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.79942836660118710928733936389534291771 at Left Supraclinoid Internal Carotid Artery with center x=285, y=215\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.80048101091444895066772572129871971243 at Anterior Communicating Artery with center x=222, y=225\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.80114244849666367523293067199486077713 at Left Anterior Cerebral Artery with center x=258, y=150\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.80114244849666367523293067199486077713 at Anterior Communicating Artery with center x=253, y=204\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.80190289468142266421549927426167714158 at Left Middle Cerebral Artery with center x=447, y=280\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.80461517820710375402982229582943598734 at Left Supraclinoid Internal Carotid Artery with center x=262, y=231\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.81098958708250149437576237811675033160 at Right Anterior Cerebral Artery with center x=246, y=179\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.82247540847692847800462620079965863384 at Other Posterior Circulation with center x=242, y=288\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.82641698422464356104108563099150990855 at Other Posterior Circulation with center x=280, y=242\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.82641698422464356104108563099150990855 at Other Posterior Circulation with center x=276, y=246\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.82641698422464356104108563099150990855 at Other Posterior Circulation with center x=271, y=253\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.84908441442551598157537604822760711232 at Left Middle Cerebral Artery with center x=142, y=118\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.84955070686251417902923705821409495324 at Anterior Communicating Artery with center x=250, y=246\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.85709849872024108265120796348331660195 at Right Supraclinoid Internal Carotid Artery with center x=235, y=301\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.86822530556046989269633487715061058236 at Left Posterior Communicating Artery with center x=279, y=216\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.87794163393266428648659243169230666286 at Anterior Communicating Artery with center x=226, y=228\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.88044882887797890422716086408658477347 at Right Middle Cerebral Artery with center x=159, y=208\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.88044882887797890422716086408658477347 at Anterior Communicating Artery with center x=257, y=203\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.88512241250207324783783101806489145581 at Right Supraclinoid Internal Carotid Artery with center x=235, y=235\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763 at Right Anterior Cerebral Artery with center x=116, y=106\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763 at Anterior Communicating Artery with center x=125, y=91\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763 at Right Anterior Cerebral Artery with center x=120, y=106\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763 at Anterior Communicating Artery with center x=123, y=89\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763 at Right Anterior Cerebral Artery with center x=133, y=76\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.88739296218460643753583291722714541935 at Right Anterior Cerebral Artery with center x=244, y=191\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.88905360377095450551559885185901908404 at Left Supraclinoid Internal Carotid Artery with center x=281, y=229\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.89421386426320866039573378582181968701 at Left Infraclinoid Internal Carotid Artery with center x=270, y=212\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.89421386426320866039573378582181968701 at Left Infraclinoid Internal Carotid Artery with center x=281, y=216\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.89990837914171555676446644356114244393 at Basilar Tip with center x=232, y=288\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.90015157820692758596783999454928886688 at Left Supraclinoid Internal Carotid Artery with center x=269, y=221\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.90168683694094931217787644438845074017 at Right Middle Cerebral Artery with center x=49, y=106\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.92418959634964175917370213963992652610 at Anterior Communicating Artery with center x=252, y=210\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.92543328866053664733167983708344898988 at Left Infraclinoid Internal Carotid Artery with center x=137, y=104\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.92773748942952645243074808740855383414 at Right Infraclinoid Internal Carotid Artery with center x=207, y=227\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.96218477847514569819859044953648183121 at Right Supraclinoid Internal Carotid Artery with center x=225, y=218\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.97057911327885502714270510313728134927 at Right Middle Cerebral Artery with center x=176, y=199\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.97256479550884529885940791074752719030 at Anterior Communicating Artery with center x=252, y=207\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.97256479550884529885940791074752719030 at Left Supraclinoid Internal Carotid Artery with center x=276, y=216\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.98123758735027035609698227781754927939 at Left Posterior Communicating Artery with center x=292, y=286\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.98123758735027035609698227781754927939 at Left Supraclinoid Internal Carotid Artery with center x=276, y=299\nSaved localized ROI for 1.2.826.0.1.3680043.8.498.98133633346919790888527055899070500258 at Right Supraclinoid Internal Carotid Artery with center x=113, y=125\n","output_type":"stream"}],"execution_count":71},{"cell_type":"code","source":"!pip install pyradiomics","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:01:21.058776Z","iopub.execute_input":"2025-10-13T16:01:21.059092Z","iopub.status.idle":"2025-10-13T16:01:24.335093Z","shell.execute_reply.started":"2025-10-13T16:01:21.059063Z","shell.execute_reply":"2025-10-13T16:01:24.334211Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: pyradiomics in /usr/local/lib/python3.11/dist-packages (3.0.1)\nRequirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.11/dist-packages (from pyradiomics) (1.26.4)\nRequirement already satisfied: SimpleITK>=0.9.1 in /usr/local/lib/python3.11/dist-packages (from pyradiomics) (2.5.2)\nRequirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from pyradiomics) (1.8.0)\nRequirement already satisfied: pykwalify>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from pyradiomics) (1.8.0)\nRequirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.11/dist-packages (from pyradiomics) (1.17.0)\nRequirement already satisfied: mkl_fft in /usr/local/lib/python3.11/dist-packages (from numpy>=1.9.2->pyradiomics) (1.3.8)\nRequirement already satisfied: mkl_random in /usr/local/lib/python3.11/dist-packages (from numpy>=1.9.2->pyradiomics) (1.2.4)\nRequirement already satisfied: mkl_umath in /usr/local/lib/python3.11/dist-packages (from numpy>=1.9.2->pyradiomics) (0.1.1)\nRequirement already satisfied: mkl in /usr/local/lib/python3.11/dist-packages (from numpy>=1.9.2->pyradiomics) (2025.2.0)\nRequirement already satisfied: tbb4py in /usr/local/lib/python3.11/dist-packages (from numpy>=1.9.2->pyradiomics) (2022.2.0)\nRequirement already satisfied: mkl-service in /usr/local/lib/python3.11/dist-packages (from numpy>=1.9.2->pyradiomics) (2.4.1)\nRequirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.11/dist-packages (from pykwalify>=1.6.0->pyradiomics) (0.6.2)\nRequirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.11/dist-packages (from pykwalify>=1.6.0->pyradiomics) (2.9.0.post0)\nRequirement already satisfied: ruamel.yaml>=0.16.0 in /usr/local/lib/python3.11/dist-packages (from pykwalify>=1.6.0->pyradiomics) (0.18.15)\nRequirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.11/dist-packages (from ruamel.yaml>=0.16.0->pykwalify>=1.6.0->pyradiomics) (0.2.14)\nRequirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.9.2->pyradiomics) (2024.2.0)\nRequirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.9.2->pyradiomics) (2022.2.0)\nRequirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy>=1.9.2->pyradiomics) (1.4.0)\nRequirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy>=1.9.2->pyradiomics) (2024.2.0)\nRequirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy>=1.9.2->pyradiomics) (2024.2.0)\n","output_type":"stream"}],"execution_count":64},{"cell_type":"code","source":"import os\nimport sys\nimport numpy as np\nimport pandas as pd\nimport SimpleITK as sitk\nfrom radiomics import featureextractor\n\n# Paths (align with your notebook)\nROI_OUTPUT_DIR = \"/kaggle/working/roi_extracted\"\nOUTPUT_FEATURE_CSV = \"/kaggle/working/radiomics_features_per_ROI_with_modality.csv\"\nTARGET_SPACING = (1.0, 1.0, 1.0)\n\n# Ensure series -> modality mapping exists (from your notebook)\n# Example: series_to_modality = dict(zip(df['SeriesInstanceUID'], df['Modality']))\n# Replace below with the actual dict from your notebook\n# from earlier cell: series_to_modality = dict(zip(df['SeriesInstanceUID'], df['Modality']))\ntry:\n series_to_modality\nexcept NameError:\n series_to_modality = {} # fallback; populate appropriately in notebook\n\n# Configure PyRadiomics\npyradiomics_settings = {\n 'binWidth': 25,\n 'resampledPixelSpacing': TARGET_SPACING,\n 'interpolator': sitk.sitkBSpline,\n 'normalize': True,\n 'normalizeScale': 100\n}\n\nextractor = featureextractor.RadiomicsFeatureExtractor(**pyradiomics_settings)\nextractor.enableImageTypeByName('Original')\nextractor.enableFeatureClassByName('shape')\nextractor.enableFeatureClassByName('firstorder')\nextractor.enableFeatureClassByName('glcm')\nextractor.enableFeatureClassByName('glrlm')\nextractor.enableFeatureClassByName('glszm')\nextractor.enableFeatureClassByName('gldm')\nextractor.enableFeatureClassByName('ngtdm')\n\nresults = []\ndebug_logs = []\n\n# Helper: resample mask to image grid\ndef resample_mask_to_image(mask_sitk, ref_img_sitk):\n \"\"\"Resample label image (mask) to reference image grid using nearest neighbor.\"\"\"\n if (mask_sitk.GetSize() == ref_img_sitk.GetSize()\n and mask_sitk.GetSpacing() == ref_img_sitk.GetSpacing()\n and mask_sitk.GetOrigin() == ref_img_sitk.GetOrigin()\n and mask_sitk.GetDirection() == ref_img_sitk.GetDirection()):\n return mask_sitk # already aligned\n\n resampler = sitk.ResampleImageFilter()\n resampler.SetReferenceImage(ref_img_sitk)\n resampler.SetInterpolator(sitk.sitkNearestNeighbor)\n resampled = resampler.Execute(mask_sitk)\n return resampled\n\n# Main loop\nfor location in sorted(os.listdir(ROI_OUTPUT_DIR)):\n loc_dir = os.path.join(ROI_OUTPUT_DIR, location)\n if not os.path.isdir(loc_dir):\n continue\n\n roi_images = [f for f in os.listdir(loc_dir) if f.endswith('_image.nii.gz')]\n for img_fname in sorted(roi_images):\n series_uid = img_fname.split('_ROI_')[0]\n modality = series_to_modality.get(series_uid, 'Unknown')\n img_path = os.path.join(loc_dir, img_fname)\n mask_path = img_path.replace('_image.nii.gz', '_mask.nii.gz')\n\n if not os.path.exists(mask_path):\n msg = f\"Mask missing for {img_path}; skipping.\"\n print(msg)\n debug_logs.append(msg)\n continue\n\n # Load image and mask SITK objects\n try:\n img_sitk = sitk.ReadImage(img_path)\n except Exception as e:\n msg = f\"Failed to read image {img_path}: {e}\"\n print(msg)\n debug_logs.append(msg)\n continue\n\n try:\n mask_sitk = sitk.ReadImage(mask_path)\n except Exception as e:\n msg = f\"Failed to read mask {mask_path}: {e}\"\n print(msg)\n debug_logs.append(msg)\n continue\n\n # Debug shapes\n img_arr = sitk.GetArrayFromImage(img_sitk)\n mask_arr = sitk.GetArrayFromImage(mask_sitk)\n msg = f\"DEBUG: series={series_uid} location={location} image_shape={img_arr.shape} mask_shape={mask_arr.shape}\"\n print(msg)\n debug_logs.append(msg)\n\n # Resample mask to image space if geometry mismatch\n if (img_sitk.GetSize() != mask_sitk.GetSize()\n or img_sitk.GetSpacing() != mask_sitk.GetSpacing()\n or img_sitk.GetOrigin() != mask_sitk.GetOrigin()\n or img_sitk.GetDirection() != mask_sitk.GetDirection()):\n msg = f\"DEBUG: geometry mismatch for {series_uid} - resampling mask to image grid\"\n print(msg)\n debug_logs.append(msg)\n mask_sitk = resample_mask_to_image(mask_sitk, img_sitk)\n mask_arr = sitk.GetArrayFromImage(mask_sitk)\n msg = f\"DEBUG: post-resample mask_shape={mask_arr.shape} spacing={mask_sitk.GetSpacing()}\"\n print(msg)\n debug_logs.append(msg)\n\n # Binarize mask: map all >0 values to 1 (label expected by PyRadiomics)\n mask_bin = (mask_arr > 0).astype(np.uint8)\n\n # Check non-empty\n if mask_bin.max() == 0:\n msg = f\"WARNING: empty mask after binarization for {mask_path}; skipping.\"\n print(msg)\n debug_logs.append(msg)\n continue\n\n # Convert binarized mask back to SITK and copy spatial info from resampled mask\n mask_sitk_bin = sitk.GetImageFromArray(mask_bin)\n mask_sitk_bin.CopyInformation(mask_sitk)\n\n # Final label check\n unique_labels = np.unique(sitk.GetArrayFromImage(mask_sitk_bin))\n msg = f\"DEBUG: unique labels (after prepare) = {unique_labels} for {mask_path}\"\n print(msg)\n debug_logs.append(msg)\n\n # Extract features (explicitly pass label=1 to be safe)\n try:\n feats = extractor.execute(img_sitk, mask_sitk_bin, label=1)\n except Exception as e:\n msg = f\"ERROR: extractor failed for {series_uid}, {location}: {e}\"\n print(msg)\n debug_logs.append(msg)\n continue\n\n # Flatten features and keep numeric values\n feat_row = {k: float(v) for k, v in feats.items() if isinstance(v, (int, float, np.floating, np.integer))}\n feat_row['SeriesInstanceUID'] = series_uid\n feat_row['Location'] = location\n feat_row['Modality'] = modality\n\n results.append(feat_row)\n\n# Save results and debug log\ndf = pd.DataFrame(results)\ndf.to_csv(OUTPUT_FEATURE_CSV, index=False)\nprint(f\"Saved {len(df)} ROI feature rows to {OUTPUT_FEATURE_CSV}\")\n\n# Optional: write debug logs for later inspection\ndebug_log_path = os.path.join(os.path.dirname(OUTPUT_FEATURE_CSV), \"radiomics_debug.log\")\nwith open(debug_log_path, \"w\") as fh:\n for ln in debug_logs:\n fh.write(ln + \"\\n\")\nprint(f\"Debug log saved to {debug_log_path}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:23:16.186113Z","iopub.execute_input":"2025-10-13T16:23:16.186501Z","iopub.status.idle":"2025-10-13T16:26:54.320095Z","shell.execute_reply.started":"2025-10-13T16:23:16.18648Z","shell.execute_reply":"2025-10-13T16:26:54.319279Z"}},"outputs":[{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11422928060228360802778018026859204182 location=Anterior_Communicating_Artery image_shape=(217, 40, 40) mask_shape=(217, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.11422928060228360802778018026859204182_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11447542941959800581541313722844637822 location=Anterior_Communicating_Artery image_shape=(472, 40, 40) mask_shape=(472, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.11447542941959800581541313722844637822_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11557464859397815362951522785245632020 location=Anterior_Communicating_Artery image_shape=(47, 40, 40) mask_shape=(47, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.11557464859397815362951522785245632020_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\nGLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11999987145696510072091906561590137848 location=Anterior_Communicating_Artery image_shape=(188, 40, 40) mask_shape=(188, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.11999987145696510072091906561590137848_ROI_Anterior_Communicating_Artery_mask.nii.gz\nDEBUG: series=1.2.826.0.1.3680043.8.498.12773309706735630359315214846273921394 location=Anterior_Communicating_Artery image_shape=(42, 40, 40) mask_shape=(42, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.12773309706735630359315214846273921394_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12896910506681881306246412668919668702 location=Anterior_Communicating_Artery image_shape=(28, 40, 40) mask_shape=(28, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.12896910506681881306246412668919668702_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.13359737970612926494907045108541390310 location=Anterior_Communicating_Artery image_shape=(278, 40, 40) mask_shape=(278, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.13359737970612926494907045108541390310_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.13789305723712362238118274295587312089 location=Anterior_Communicating_Artery image_shape=(74, 40, 40) mask_shape=(74, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.13789305723712362238118274295587312089_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.16386250344855221757144432829845114733 location=Anterior_Communicating_Artery image_shape=(158, 40, 40) mask_shape=(158, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.16386250344855221757144432829845114733_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.16984390277144742906667579449023180512 location=Anterior_Communicating_Artery image_shape=(382, 40, 40) mask_shape=(382, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.16984390277144742906667579449023180512_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.23047023542526806696555440426928375679 location=Anterior_Communicating_Artery image_shape=(341, 40, 40) mask_shape=(341, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.23047023542526806696555440426928375679_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.23055827202917388669053993133576833763 location=Anterior_Communicating_Artery image_shape=(30, 40, 40) mask_shape=(30, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.23055827202917388669053993133576833763_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.35378146560080702211693278243609271022 location=Anterior_Communicating_Artery image_shape=(40, 40, 40) mask_shape=(40, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.35378146560080702211693278243609271022_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.36928611823925733133253145871406408988 location=Anterior_Communicating_Artery image_shape=(120, 40, 40) mask_shape=(120, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.36928611823925733133253145871406408988_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.39640919070091958876744231048011388614 location=Anterior_Communicating_Artery image_shape=(581, 40, 40) mask_shape=(581, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.39640919070091958876744231048011388614_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.49718418682238683779854914910561017368 location=Anterior_Communicating_Artery image_shape=(211, 40, 40) mask_shape=(211, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.49718418682238683779854914910561017368_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.50241233088534910114736887318508484246 location=Anterior_Communicating_Artery image_shape=(156, 40, 40) mask_shape=(156, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.50241233088534910114736887318508484246_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.53947155422591684879953627516013605305 location=Anterior_Communicating_Artery image_shape=(373, 40, 40) mask_shape=(373, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.53947155422591684879953627516013605305_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.54865110953409154322874363435644372368 location=Anterior_Communicating_Artery image_shape=(172, 40, 40) mask_shape=(172, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.54865110953409154322874363435644372368_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.56109731607412273442907651635753012241 location=Anterior_Communicating_Artery image_shape=(427, 40, 40) mask_shape=(427, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.56109731607412273442907651635753012241_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.56867346585094457716984380929416039466 location=Anterior_Communicating_Artery image_shape=(893, 40, 40) mask_shape=(893, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.56867346585094457716984380929416039466_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.65011208113835286935212080363533579671 location=Anterior_Communicating_Artery image_shape=(244, 40, 40) mask_shape=(244, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.65011208113835286935212080363533579671_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.68161752706586485995657009830735928975 location=Anterior_Communicating_Artery image_shape=(108, 40, 40) mask_shape=(108, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.68161752706586485995657009830735928975_ROI_Anterior_Communicating_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.68161752706586485995657009830735928975, Anterior_Communicating_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.68356160898101066850726244725552676010 location=Anterior_Communicating_Artery image_shape=(126, 40, 40) mask_shape=(126, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.68356160898101066850726244725552676010_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.68654901185438820364160878605611510817 location=Anterior_Communicating_Artery image_shape=(168, 40, 40) mask_shape=(168, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.68654901185438820364160878605611510817_ROI_Anterior_Communicating_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.68654901185438820364160878605611510817, Anterior_Communicating_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.69401690945645968072368812538918487252 location=Anterior_Communicating_Artery image_shape=(216, 40, 40) mask_shape=(216, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.69401690945645968072368812538918487252_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.70243202242722756546202582478829903758 location=Anterior_Communicating_Artery image_shape=(45, 40, 40) mask_shape=(45, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.70243202242722756546202582478829903758_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.75294325392457179365040684378207706807 location=Anterior_Communicating_Artery image_shape=(164, 40, 40) mask_shape=(164, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.75294325392457179365040684378207706807_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.80048101091444895066772572129871971243 location=Anterior_Communicating_Artery image_shape=(73, 40, 40) mask_shape=(73, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.80048101091444895066772572129871971243_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.80114244849666367523293067199486077713 location=Anterior_Communicating_Artery image_shape=(301, 40, 40) mask_shape=(301, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.80114244849666367523293067199486077713_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.84955070686251417902923705821409495324 location=Anterior_Communicating_Artery image_shape=(446, 40, 40) mask_shape=(446, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.84955070686251417902923705821409495324_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.87794163393266428648659243169230666286 location=Anterior_Communicating_Artery image_shape=(274, 40, 40) mask_shape=(274, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.87794163393266428648659243169230666286_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.88044882887797890422716086408658477347 location=Anterior_Communicating_Artery image_shape=(181, 40, 40) mask_shape=(181, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.88044882887797890422716086408658477347_ROI_Anterior_Communicating_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.88044882887797890422716086408658477347, Anterior_Communicating_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763 location=Anterior_Communicating_Artery image_shape=(160, 40, 40) mask_shape=(160, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763_ROI_Anterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.92418959634964175917370213963992652610 location=Anterior_Communicating_Artery image_shape=(24, 40, 40) mask_shape=(24, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.92418959634964175917370213963992652610_ROI_Anterior_Communicating_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.92418959634964175917370213963992652610, Anterior_Communicating_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.97256479550884529885940791074752719030 location=Anterior_Communicating_Artery image_shape=(214, 40, 40) mask_shape=(214, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Anterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.97256479550884529885940791074752719030_ROI_Anterior_Communicating_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.97256479550884529885940791074752719030, Anterior_Communicating_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.11924949819899884502738782576851659426 location=Basilar_Tip image_shape=(246, 40, 40) mask_shape=(246, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.11924949819899884502738782576851659426_ROI_Basilar_Tip_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12226380705607315060235896835122737788 location=Basilar_Tip image_shape=(272, 40, 40) mask_shape=(272, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.12226380705607315060235896835122737788_ROI_Basilar_Tip_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\nGLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.15412988336827906186857260013885503248 location=Basilar_Tip image_shape=(18, 40, 40) mask_shape=(18, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.15412988336827906186857260013885503248_ROI_Basilar_Tip_mask.nii.gz\nDEBUG: series=1.2.826.0.1.3680043.8.498.27693546360513068451517048347207987807 location=Basilar_Tip image_shape=(35, 40, 40) mask_shape=(35, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.27693546360513068451517048347207987807_ROI_Basilar_Tip_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\nGLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.28722601444191262075880952461419085326 location=Basilar_Tip image_shape=(152, 40, 40) mask_shape=(152, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.28722601444191262075880952461419085326_ROI_Basilar_Tip_mask.nii.gz\nDEBUG: series=1.2.826.0.1.3680043.8.498.32250259987224176174516959348681094310 location=Basilar_Tip image_shape=(21, 40, 40) mask_shape=(21, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.32250259987224176174516959348681094310_ROI_Basilar_Tip_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.32250259987224176174516959348681094310, Basilar_Tip: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.36563348911961346172279351080943665664 location=Basilar_Tip image_shape=(120, 40, 40) mask_shape=(120, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.36563348911961346172279351080943665664_ROI_Basilar_Tip_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.42092450058597943280470345107435382425 location=Basilar_Tip image_shape=(255, 40, 40) mask_shape=(255, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.42092450058597943280470345107435382425_ROI_Basilar_Tip_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.49640345168968922611291772802640560828 location=Basilar_Tip image_shape=(19, 40, 40) mask_shape=(19, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.49640345168968922611291772802640560828_ROI_Basilar_Tip_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.49640345168968922611291772802640560828, Basilar_Tip: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.68356160898101066850726244725552676010 location=Basilar_Tip image_shape=(126, 40, 40) mask_shape=(126, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.68356160898101066850726244725552676010_ROI_Basilar_Tip_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.75294325392457179365040684378207706807 location=Basilar_Tip image_shape=(164, 40, 40) mask_shape=(164, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.75294325392457179365040684378207706807_ROI_Basilar_Tip_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\nGLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.89990837914171555676446644356114244393 location=Basilar_Tip image_shape=(18, 40, 40) mask_shape=(18, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Basilar_Tip/1.2.826.0.1.3680043.8.498.89990837914171555676446644356114244393_ROI_Basilar_Tip_mask.nii.gz\nDEBUG: series=1.2.826.0.1.3680043.8.498.80114244849666367523293067199486077713 location=Left_Anterior_Cerebral_Artery image_shape=(301, 40, 40) mask_shape=(301, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Anterior_Cerebral_Artery/1.2.826.0.1.3680043.8.498.80114244849666367523293067199486077713_ROI_Left_Anterior_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.10188636688783982623025997809119805350 location=Left_Infraclinoid_Internal_Carotid_Artery image_shape=(136, 40, 40) mask_shape=(136, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Left_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.10188636688783982623025997809119805350_ROI_Left_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.10188636688783982623025997809119805350, Left_Infraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.11140496970152788589837488009637704168 location=Left_Infraclinoid_Internal_Carotid_Artery image_shape=(36, 40, 40) mask_shape=(36, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.11140496970152788589837488009637704168_ROI_Left_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12898332622076283462996059479076432725 location=Left_Infraclinoid_Internal_Carotid_Artery image_shape=(607, 40, 40) mask_shape=(607, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.12898332622076283462996059479076432725_ROI_Left_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.67256382079119118825371537284628604044 location=Left_Infraclinoid_Internal_Carotid_Artery image_shape=(36, 40, 40) mask_shape=(36, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.67256382079119118825371537284628604044_ROI_Left_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.79942836660118710928733936389534291771 location=Left_Infraclinoid_Internal_Carotid_Artery image_shape=(201, 40, 40) mask_shape=(201, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.79942836660118710928733936389534291771_ROI_Left_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.89421386426320866039573378582181968701 location=Left_Infraclinoid_Internal_Carotid_Artery image_shape=(440, 40, 40) mask_shape=(440, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Left_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.89421386426320866039573378582181968701_ROI_Left_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.89421386426320866039573378582181968701, Left_Infraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.92543328866053664733167983708344898988 location=Left_Infraclinoid_Internal_Carotid_Artery image_shape=(160, 40, 40) mask_shape=(160, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.92543328866053664733167983708344898988_ROI_Left_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381 location=Left_Middle_Cerebral_Artery image_shape=(228, 40, 40) mask_shape=(228, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381_ROI_Left_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12873050136415197430227722045995986358 location=Left_Middle_Cerebral_Artery image_shape=(45, 40, 40) mask_shape=(45, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.12873050136415197430227722045995986358_ROI_Left_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.31897325247898403027455884342546675049 location=Left_Middle_Cerebral_Artery image_shape=(642, 40, 40) mask_shape=(642, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.31897325247898403027455884342546675049_ROI_Left_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.77257791208759842602760935296318202703 location=Left_Middle_Cerebral_Artery image_shape=(380, 40, 40) mask_shape=(380, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.77257791208759842602760935296318202703_ROI_Left_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.80190289468142266421549927426167714158 location=Left_Middle_Cerebral_Artery image_shape=(217, 40, 40) mask_shape=(217, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Left_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.80190289468142266421549927426167714158_ROI_Left_Middle_Cerebral_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.80190289468142266421549927426167714158, Left_Middle_Cerebral_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.84908441442551598157537604822760711232 location=Left_Middle_Cerebral_Artery image_shape=(256, 40, 40) mask_shape=(256, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.84908441442551598157537604822760711232_ROI_Left_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382 location=Left_Posterior_Communicating_Artery image_shape=(289, 40, 40) mask_shape=(289, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382_ROI_Left_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.19915189891686122627071348069843885714 location=Left_Posterior_Communicating_Artery image_shape=(222, 40, 40) mask_shape=(222, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Left_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.19915189891686122627071348069843885714_ROI_Left_Posterior_Communicating_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.19915189891686122627071348069843885714, Left_Posterior_Communicating_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.38245669369430321272819874468980907728 location=Left_Posterior_Communicating_Artery image_shape=(120, 40, 40) mask_shape=(120, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.38245669369430321272819874468980907728_ROI_Left_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.66341469849558089736451534296312923277 location=Left_Posterior_Communicating_Artery image_shape=(494, 40, 40) mask_shape=(494, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.66341469849558089736451534296312923277_ROI_Left_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.75798029534455454939797323020706657426 location=Left_Posterior_Communicating_Artery image_shape=(83, 40, 40) mask_shape=(83, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.75798029534455454939797323020706657426_ROI_Left_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.86822530556046989269633487715061058236 location=Left_Posterior_Communicating_Artery image_shape=(414, 40, 40) mask_shape=(414, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.86822530556046989269633487715061058236_ROI_Left_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.98123758735027035609698227781754927939 location=Left_Posterior_Communicating_Artery image_shape=(61, 40, 40) mask_shape=(61, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.98123758735027035609698227781754927939_ROI_Left_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11163718560814217911019576488539324434 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(114, 40, 40) mask_shape=(114, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.11163718560814217911019576488539324434_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11821446229980500432989393232863242415 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(959, 40, 40) mask_shape=(959, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.11821446229980500432989393232863242415_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12888459003897616398890411591973176636 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(75, 40, 40) mask_shape=(75, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.12888459003897616398890411591973176636_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.14375161350968928494386548917647435597 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(176, 40, 40) mask_shape=(176, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.14375161350968928494386548917647435597_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.14375161350968928494386548917647435597, Left_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.15777485274723969278718374949878560903 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(240, 40, 40) mask_shape=(240, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.15777485274723969278718374949878560903_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.16984390277144742906667579449023180512 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(382, 40, 40) mask_shape=(382, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.16984390277144742906667579449023180512_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.24023896361071846724104915533800547445 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(21, 40, 40) mask_shape=(21, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.24023896361071846724104915533800547445_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.24023896361071846724104915533800547445, Left_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.27857528510177554953207997404329765760 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(108, 40, 40) mask_shape=(108, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.27857528510177554953207997404329765760_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.27857528510177554953207997404329765760, Left_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.36516744229109249667702200145077143886 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(256, 40, 40) mask_shape=(256, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.36516744229109249667702200145077143886_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.43536331102142701793144520859521601945 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(123, 40, 40) mask_shape=(123, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.43536331102142701793144520859521601945_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.47622062519393262272120105951011625928 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(465, 40, 40) mask_shape=(465, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.47622062519393262272120105951011625928_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(60, 40, 40) mask_shape=(60, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\nGLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.54865110953409154322874363435644372368 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(172, 40, 40) mask_shape=(172, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.54865110953409154322874363435644372368_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.56479623144539472445940519727300319231 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(96, 40, 40) mask_shape=(96, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.56479623144539472445940519727300319231_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nDEBUG: series=1.2.826.0.1.3680043.8.498.56867346585094457716984380929416039466 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(893, 40, 40) mask_shape=(893, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.56867346585094457716984380929416039466_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.68654901185438820364160878605611510817 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(168, 40, 40) mask_shape=(168, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.68654901185438820364160878605611510817_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.68654901185438820364160878605611510817, Left_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.79942836660118710928733936389534291771 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(201, 40, 40) mask_shape=(201, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.79942836660118710928733936389534291771_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.80461517820710375402982229582943598734 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(249, 40, 40) mask_shape=(249, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.80461517820710375402982229582943598734_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.88905360377095450551559885185901908404 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(266, 40, 40) mask_shape=(266, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.88905360377095450551559885185901908404_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.90015157820692758596783999454928886688 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(24, 40, 40) mask_shape=(24, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.90015157820692758596783999454928886688_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.97256479550884529885940791074752719030 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(214, 40, 40) mask_shape=(214, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.97256479550884529885940791074752719030_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.97256479550884529885940791074752719030, Left_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.98123758735027035609698227781754927939 location=Left_Supraclinoid_Internal_Carotid_Artery image_shape=(61, 40, 40) mask_shape=(61, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Left_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.98123758735027035609698227781754927939_ROI_Left_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382 location=Other_Posterior_Circulation image_shape=(289, 40, 40) mask_shape=(289, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11639720015527164474926997755882681707 location=Other_Posterior_Circulation image_shape=(40, 40, 40) mask_shape=(40, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.11639720015527164474926997755882681707_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11641438607169452758239778414614826230 location=Other_Posterior_Circulation image_shape=(30, 40, 40) mask_shape=(30, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.11641438607169452758239778414614826230_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11924949819899884502738782576851659426 location=Other_Posterior_Circulation image_shape=(246, 40, 40) mask_shape=(246, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.11924949819899884502738782576851659426_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12180351938456969219537687190067731477 location=Other_Posterior_Circulation image_shape=(196, 40, 40) mask_shape=(196, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.12180351938456969219537687190067731477_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12914952223659958493995413641114579279 location=Other_Posterior_Circulation image_shape=(30, 40, 40) mask_shape=(30, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.12914952223659958493995413641114579279_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.15111820005882064793593034423469604305 location=Other_Posterior_Circulation image_shape=(70, 40, 40) mask_shape=(70, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.15111820005882064793593034423469604305_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\nGLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.17415277997649872560329721717694101082 location=Other_Posterior_Circulation image_shape=(188, 40, 40) mask_shape=(188, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.17415277997649872560329721717694101082_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.19915189891686122627071348069843885714 location=Other_Posterior_Circulation image_shape=(222, 40, 40) mask_shape=(222, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.19915189891686122627071348069843885714_ROI_Other_Posterior_Circulation_mask.nii.gz\nDEBUG: series=1.2.826.0.1.3680043.8.498.34439485184360273751379923196589017042 location=Other_Posterior_Circulation image_shape=(27, 40, 40) mask_shape=(27, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.34439485184360273751379923196589017042_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.38904475631578710113273863766282479811 location=Other_Posterior_Circulation image_shape=(302, 40, 40) mask_shape=(302, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.38904475631578710113273863766282479811_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.40402571428459178954472078378902050472 location=Other_Posterior_Circulation image_shape=(455, 40, 40) mask_shape=(455, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.40402571428459178954472078378902050472_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.42672154202952548010999212369080894652 location=Other_Posterior_Circulation image_shape=(184, 40, 40) mask_shape=(184, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.42672154202952548010999212369080894652_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.58839417089022860359638460482101293080 location=Other_Posterior_Circulation image_shape=(78, 40, 40) mask_shape=(78, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.58839417089022860359638460482101293080_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.61152918475243358118286003299125054478 location=Other_Posterior_Circulation image_shape=(281, 40, 40) mask_shape=(281, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.61152918475243358118286003299125054478_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.73820261697830420042473892884688067574 location=Other_Posterior_Circulation image_shape=(494, 40, 40) mask_shape=(494, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.73820261697830420042473892884688067574_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.79099213587801933936080747802403048718 location=Other_Posterior_Circulation image_shape=(64, 40, 40) mask_shape=(64, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.79099213587801933936080747802403048718_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.82247540847692847800462620079965863384 location=Other_Posterior_Circulation image_shape=(272, 40, 40) mask_shape=(272, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.82247540847692847800462620079965863384_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.82641698422464356104108563099150990855 location=Other_Posterior_Circulation image_shape=(867, 40, 40) mask_shape=(867, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Other_Posterior_Circulation/1.2.826.0.1.3680043.8.498.82641698422464356104108563099150990855_ROI_Other_Posterior_Circulation_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381 location=Right_Anterior_Cerebral_Artery image_shape=(228, 40, 40) mask_shape=(228, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Anterior_Cerebral_Artery/1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381_ROI_Right_Anterior_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12896910506681881306246412668919668702 location=Right_Anterior_Cerebral_Artery image_shape=(28, 40, 40) mask_shape=(28, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Anterior_Cerebral_Artery/1.2.826.0.1.3680043.8.498.12896910506681881306246412668919668702_ROI_Right_Anterior_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.16386250344855221757144432829845114733 location=Right_Anterior_Cerebral_Artery image_shape=(158, 40, 40) mask_shape=(158, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Anterior_Cerebral_Artery/1.2.826.0.1.3680043.8.498.16386250344855221757144432829845114733_ROI_Right_Anterior_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.21004106426734635526381567602936015568 location=Right_Anterior_Cerebral_Artery image_shape=(378, 40, 40) mask_shape=(378, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Anterior_Cerebral_Artery/1.2.826.0.1.3680043.8.498.21004106426734635526381567602936015568_ROI_Right_Anterior_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.47802313478131783077762931281303667601 location=Right_Anterior_Cerebral_Artery image_shape=(204, 40, 40) mask_shape=(204, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Anterior_Cerebral_Artery/1.2.826.0.1.3680043.8.498.47802313478131783077762931281303667601_ROI_Right_Anterior_Cerebral_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.47802313478131783077762931281303667601, Right_Anterior_Cerebral_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.68276712082656957005274595949315894066 location=Right_Anterior_Cerebral_Artery image_shape=(176, 40, 40) mask_shape=(176, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Anterior_Cerebral_Artery/1.2.826.0.1.3680043.8.498.68276712082656957005274595949315894066_ROI_Right_Anterior_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.81098958708250149437576237811675033160 location=Right_Anterior_Cerebral_Artery image_shape=(60, 40, 40) mask_shape=(60, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Anterior_Cerebral_Artery/1.2.826.0.1.3680043.8.498.81098958708250149437576237811675033160_ROI_Right_Anterior_Cerebral_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.81098958708250149437576237811675033160, Right_Anterior_Cerebral_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763 location=Right_Anterior_Cerebral_Artery image_shape=(160, 40, 40) mask_shape=(160, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Anterior_Cerebral_Artery/1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763_ROI_Right_Anterior_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.88739296218460643753583291722714541935 location=Right_Anterior_Cerebral_Artery image_shape=(43, 40, 40) mask_shape=(43, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Anterior_Cerebral_Artery/1.2.826.0.1.3680043.8.498.88739296218460643753583291722714541935_ROI_Right_Anterior_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12271269630687930751200307891697907423 location=Right_Infraclinoid_Internal_Carotid_Artery image_shape=(242, 40, 40) mask_shape=(242, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.12271269630687930751200307891697907423_ROI_Right_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.12271269630687930751200307891697907423, Right_Infraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.12427930128533148989436011949311706348 location=Right_Infraclinoid_Internal_Carotid_Artery image_shape=(120, 40, 40) mask_shape=(120, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.12427930128533148989436011949311706348_ROI_Right_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.50369188120242587742908379292729868174 location=Right_Infraclinoid_Internal_Carotid_Artery image_shape=(220, 40, 40) mask_shape=(220, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.50369188120242587742908379292729868174_ROI_Right_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.50369188120242587742908379292729868174, Right_Infraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.92773748942952645243074808740855383414 location=Right_Infraclinoid_Internal_Carotid_Artery image_shape=(551, 40, 40) mask_shape=(551, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Infraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.92773748942952645243074808740855383414_ROI_Right_Infraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\nGLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.10410600166004340343973545138447283460 location=Right_Middle_Cerebral_Artery image_shape=(95, 40, 40) mask_shape=(95, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.10410600166004340343973545138447283460_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\nDEBUG: series=1.2.826.0.1.3680043.8.498.10540586847553109495238524904638776495 location=Right_Middle_Cerebral_Artery image_shape=(21, 40, 40) mask_shape=(21, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.10540586847553109495238524904638776495_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.10540586847553109495238524904638776495, Right_Middle_Cerebral_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.10929608782694347957516071062422315982 location=Right_Middle_Cerebral_Artery image_shape=(320, 40, 40) mask_shape=(320, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.10929608782694347957516071062422315982_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\nGLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11504459395565711149380261095223705023 location=Right_Middle_Cerebral_Artery image_shape=(120, 40, 40) mask_shape=(120, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.11504459395565711149380261095223705023_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\nDEBUG: series=1.2.826.0.1.3680043.8.498.11624217734793256238140178687655335066 location=Right_Middle_Cerebral_Artery image_shape=(265, 40, 40) mask_shape=(265, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.11624217734793256238140178687655335066_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.11938739392606296532297884225608408867 location=Right_Middle_Cerebral_Artery image_shape=(909, 40, 40) mask_shape=(909, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.11938739392606296532297884225608408867_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12709802490782896897031103447163443069 location=Right_Middle_Cerebral_Artery image_shape=(232, 40, 40) mask_shape=(232, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.12709802490782896897031103447163443069_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12792960392435514526913217158720555996 location=Right_Middle_Cerebral_Artery image_shape=(164, 40, 40) mask_shape=(164, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.12792960392435514526913217158720555996_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12812390336793304037901571645929430100 location=Right_Middle_Cerebral_Artery image_shape=(144, 40, 40) mask_shape=(144, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.12812390336793304037901571645929430100_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.24941924992372724575490063788348447936 location=Right_Middle_Cerebral_Artery image_shape=(176, 40, 40) mask_shape=(176, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.24941924992372724575490063788348447936_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.35327124657045713676192746001247576881 location=Right_Middle_Cerebral_Artery image_shape=(60, 40, 40) mask_shape=(60, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.35327124657045713676192746001247576881_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.37086262716517957668471635372810376638 location=Right_Middle_Cerebral_Artery image_shape=(237, 40, 40) mask_shape=(237, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.37086262716517957668471635372810376638_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.39640919070091958876744231048011388614 location=Right_Middle_Cerebral_Artery image_shape=(581, 40, 40) mask_shape=(581, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.39640919070091958876744231048011388614_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.50268462808449401128173812870329002342 location=Right_Middle_Cerebral_Artery image_shape=(289, 40, 40) mask_shape=(289, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.50268462808449401128173812870329002342_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.65654303333996310125136982540737772052 location=Right_Middle_Cerebral_Artery image_shape=(280, 40, 40) mask_shape=(280, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.65654303333996310125136982540737772052_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.69568746915553014138135720681936366640 location=Right_Middle_Cerebral_Artery image_shape=(222, 40, 40) mask_shape=(222, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.69568746915553014138135720681936366640_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.69568746915553014138135720681936366640, Right_Middle_Cerebral_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.77257791208759842602760935296318202703 location=Right_Middle_Cerebral_Artery image_shape=(380, 40, 40) mask_shape=(380, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.77257791208759842602760935296318202703_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\nGLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.88044882887797890422716086408658477347 location=Right_Middle_Cerebral_Artery image_shape=(181, 40, 40) mask_shape=(181, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.88044882887797890422716086408658477347_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\nDEBUG: series=1.2.826.0.1.3680043.8.498.90168683694094931217787644438845074017 location=Right_Middle_Cerebral_Artery image_shape=(38, 40, 40) mask_shape=(38, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.90168683694094931217787644438845074017_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.97057911327885502714270510313728134927 location=Right_Middle_Cerebral_Artery image_shape=(30, 40, 40) mask_shape=(30, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Middle_Cerebral_Artery/1.2.826.0.1.3680043.8.498.97057911327885502714270510313728134927_ROI_Right_Middle_Cerebral_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382 location=Right_Posterior_Communicating_Artery image_shape=(289, 40, 40) mask_shape=(289, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382_ROI_Right_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.13359737970612926494907045108541390310 location=Right_Posterior_Communicating_Artery image_shape=(278, 40, 40) mask_shape=(278, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.13359737970612926494907045108541390310_ROI_Right_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.36205761227502095958293403225062705137 location=Right_Posterior_Communicating_Artery image_shape=(168, 40, 40) mask_shape=(168, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.36205761227502095958293403225062705137_ROI_Right_Posterior_Communicating_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.36205761227502095958293403225062705137, Right_Posterior_Communicating_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.38245669369430321272819874468980907728 location=Right_Posterior_Communicating_Artery image_shape=(120, 40, 40) mask_shape=(120, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.38245669369430321272819874468980907728_ROI_Right_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430 location=Right_Posterior_Communicating_Artery image_shape=(60, 40, 40) mask_shape=(60, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430_ROI_Right_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.71796601538792777580416179841706319140 location=Right_Posterior_Communicating_Artery image_shape=(258, 40, 40) mask_shape=(258, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.71796601538792777580416179841706319140_ROI_Right_Posterior_Communicating_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.75016896260047968433534297207591136672 location=Right_Posterior_Communicating_Artery image_shape=(96, 40, 40) mask_shape=(96, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Posterior_Communicating_Artery/1.2.826.0.1.3680043.8.498.75016896260047968433534297207591136672_ROI_Right_Posterior_Communicating_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.75016896260047968433534297207591136672, Right_Posterior_Communicating_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(228, 40, 40) mask_shape=(228, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.10076056930521523789588901704956188485 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(136, 40, 40) mask_shape=(136, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.10076056930521523789588901704956188485_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.10076056930521523789588901704956188485, Right_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.11624217734793256238140178687655335066 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(265, 40, 40) mask_shape=(265, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.11624217734793256238140178687655335066_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.12132622846836853200891705613461466627 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(136, 40, 40) mask_shape=(136, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.12132622846836853200891705613461466627_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.12132622846836853200891705613461466627, Right_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.12904246053955178641505906243733756576 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(240, 40, 40) mask_shape=(240, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.12904246053955178641505906243733756576_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.13128656559176299272467358793386537400 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(560, 40, 40) mask_shape=(560, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.13128656559176299272467358793386537400_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.24587963869128721940158079207224095554 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(805, 40, 40) mask_shape=(805, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.24587963869128721940158079207224095554_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.38904475631578710113273863766282479811 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(302, 40, 40) mask_shape=(302, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.38904475631578710113273863766282479811_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.42672154202952548010999212369080894652 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(184, 40, 40) mask_shape=(184, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.42672154202952548010999212369080894652_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.42672154202952548010999212369080894652, Right_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.42933230680553480084056393591634621848 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(240, 40, 40) mask_shape=(240, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.42933230680553480084056393591634621848_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.43495968397556043698567120038117641587 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(512, 40, 10) mask_shape=(512, 40, 10)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.43495968397556043698567120038117641587_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.47887093599897399482447594752785316358 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(96, 40, 40) mask_shape=(96, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.47887093599897399482447594752785316358_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.47887093599897399482447594752785316358, Right_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.50275403170194436966991630938339966596 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(21, 40, 40) mask_shape=(21, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.50275403170194436966991630938339966596_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.50275403170194436966991630938339966596, Right_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(60, 40, 40) mask_shape=(60, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.55051557363776453883164282380323354147 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(152, 40, 40) mask_shape=(152, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.55051557363776453883164282380323354147_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.55051557363776453883164282380323354147, Right_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.55520651046049733868642268089599441721 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(192, 40, 40) mask_shape=(192, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.55520651046049733868642268089599441721_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.68709340002397343932718258443293606585 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(192, 40, 40) mask_shape=(192, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.68709340002397343932718258443293606585_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.68709340002397343932718258443293606585, Right_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.72679260079421518845786364620483278827 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(305, 40, 40) mask_shape=(305, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.72679260079421518845786364620483278827_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.85709849872024108265120796348331660195 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(152, 40, 40) mask_shape=(152, 40, 40)\nDEBUG: unique labels (after prepare) = [1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.85709849872024108265120796348331660195_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\nERROR: extractor failed for 1.2.826.0.1.3680043.8.498.85709849872024108265120796348331660195, Right_Supraclinoid_Internal_Carotid_Artery: No labels found in this mask (i.e. nothing is segmented)!\nDEBUG: series=1.2.826.0.1.3680043.8.498.88512241250207324783783101806489145581 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(96, 40, 40) mask_shape=(96, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.88512241250207324783783101806489145581_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.96218477847514569819859044953648183121 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(240, 40, 40) mask_shape=(240, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.96218477847514569819859044953648183121_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"DEBUG: series=1.2.826.0.1.3680043.8.498.98133633346919790888527055899070500258 location=Right_Supraclinoid_Internal_Carotid_Artery image_shape=(292, 40, 40) mask_shape=(292, 40, 40)\nDEBUG: unique labels (after prepare) = [0 1] for /kaggle/working/roi_extracted/Right_Supraclinoid_Internal_Carotid_Artery/1.2.826.0.1.3680043.8.498.98133633346919790888527055899070500258_ROI_Right_Supraclinoid_Internal_Carotid_Artery_mask.nii.gz\n","output_type":"stream"},{"name":"stderr","text":"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n","output_type":"stream"},{"name":"stdout","text":"Saved 140 ROI feature rows to /kaggle/working/radiomics_features_per_ROI_with_modality.csv\nDebug log saved to /kaggle/working/radiomics_debug.log\n","output_type":"stream"}],"execution_count":72},{"cell_type":"code","source":"class ConvolutionalTokenEmbedding(nn.Module):\n def __init__(self, in_channel, emb_dim, kernel_size, stride, padding):\n super().__init__()\n self.proj = nn.Conv2d(in_channels=in_channel, out_channels=emb_dim, kernel_size=kernel_size, stride=stride, padding=padding)\n self.LN = nn.LayerNorm(emb_dim)\n def forward(self, x):\n x = self.proj(x)\n B, C, H, W = x.shape\n x = x.reshape(B, C, -1)\n x = x.permute(0, 2, 1)\n x = self.LN(x)\n return x, H, W\n\nclass Attention(nn.Module):\n def __init__(self, cls_token, in_dim, num_heads, kernel_size, stride_q, stride_kv, padding_q, padding_kv, attn_drop_p, attn_proj_drop_p):\n super().__init__()\n self.cls_token = cls_token\n self.num_heads = num_heads\n head_dim = in_dim // num_heads\n self.scale = head_dim ** (-0.5)\n self.conv_proj_q = nn.Sequential(\n nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=kernel_size, stride=stride_q, padding=padding_q, groups=in_dim),\n nn.BatchNorm2d(num_features=in_dim),\n nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)\n )\n self.conv_proj_k = nn.Sequential(\n nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=kernel_size, stride=stride_kv, padding=padding_kv, groups=in_dim),\n nn.BatchNorm2d(num_features=in_dim),\n nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)\n )\n self.conv_proj_v = nn.Sequential(\n nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=kernel_size, stride=stride_kv, padding=padding_kv, groups=in_dim),\n nn.BatchNorm2d(num_features=in_dim),\n nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)\n )\n self.attn_drop = nn.Dropout(p=attn_drop_p)\n self.attn_proj_drop = nn.Dropout(p=attn_proj_drop_p)\n self.proj = nn.Linear(in_features=in_dim, out_features=in_dim)\n def forward(self, x, cls_token):\n B, C, _, _ = x.shape\n q = self.conv_proj_q(x).reshape(B, C, -1).permute(0, 2, 1)\n k = self.conv_proj_k(x).reshape(B, C, -1).permute(0, 2, 1)\n v = self.conv_proj_v(x).reshape(B, C, -1).permute(0, 2, 1)\n if cls_token is not None:\n q = torch.cat([cls_token, q], dim=1)\n k = torch.cat([cls_token, k], dim=1)\n v = torch.cat([cls_token, v], dim=1)\n q = q.reshape(B, -1, self.num_heads, C//self.num_heads).permute(0, 2, 1, 3)\n k = k.reshape(B, -1, self.num_heads, C//self.num_heads).permute(0, 2, 1, 3)\n v = v.reshape(B, -1, self.num_heads, C//self.num_heads).permute(0, 2, 1, 3)\n attn = ((q @ k.transpose(-2, -1)) * self.scale).softmax(dim=-1)\n attn = self.attn_drop(attn)\n attn = (attn @ v).transpose(1, 2).reshape(B, -1, C)\n attn = self.attn_proj_drop(self.proj(attn))\n return attn\n\nclass SingleTransformerLayer(nn.Module):\n def __init__(self, cls_token, emb_dim, mlp_expansion, num_heads, kernel_size, stride_q, stride_kv, padding_q, padding_kv, attn_drop_p, attn_proj_drop_p, drop_path_p, drop_p):\n super().__init__()\n self.cls_token = cls_token\n self.LN1 = nn.LayerNorm(emb_dim)\n self.attn = Attention(cls_token, emb_dim, num_heads, kernel_size, stride_q, stride_kv, padding_q, padding_kv, attn_drop_p, attn_proj_drop_p)\n self.LN2 = nn.LayerNorm(emb_dim)\n self.MLP = nn.Sequential(\n nn.Linear(in_features=emb_dim, out_features=emb_dim * mlp_expansion),\n nn.GELU(),\n nn.Dropout(p=drop_p),\n nn.Linear(in_features=emb_dim * mlp_expansion, out_features=emb_dim),\n nn.Dropout(p=drop_p),\n )\n self.drop_path = DropPath(drop_prob=drop_path_p) if drop_path_p > 0.0 else nn.Identity()\n def forward(self, x, H, W):\n B, N, C = x.shape\n res = x\n x = self.LN1(x)\n if self.cls_token is not None:\n cls_token, x = torch.split(x, [1, H*W], dim=1)\n else:\n cls_token = None\n x = x.permute(0, 2, 1).reshape(B, C, H, W)\n x = self.attn(x, cls_token)\n x = res + self.drop_path(x)\n res = x\n x = self.LN2(x)\n x = self.MLP(x)\n x = res + self.drop_path(x)\n return x\n\nclass TransformerBlock(nn.Module):\n def __init__(self, cls_token, n_layers, emb_dim, mlp_expansion, num_heads, kernel_size, stride_q, stride_kv, padding_q, padding_kv, attn_drop_p, attn_proj_drop_p, drop_path_p, drop_p):\n super().__init__()\n drop_path_rates = torch.linspace(0, drop_path_p, n_layers).tolist()\n self.layers = nn.ModuleList()\n for i in range(n_layers):\n layer = SingleTransformerLayer(cls_token, emb_dim, mlp_expansion, num_heads, kernel_size, stride_q, stride_kv, padding_q, padding_kv, attn_drop_p, attn_proj_drop_p, drop_path_rates[i], drop_p)\n self.layers.append(layer)\n def forward(self, x, H, W):\n for layer in self.layers:\n x = layer(x, H, W)\n return x\n\nclass ConvolutionalVisionTransformer(nn.Module):\n def __init__(self, in_channel=1, num_classes=512, emb_dim=[32, 64, 128], n_layers=[1, 2, 3], kernel_size=[5, 3, 3], stride=[2, 2, 2], padding=[2, 1, 1], mlp_expansion=4, num_heads=[1, 2, 4], attn_kernel_size=3, stride_q=[1, 1, 1], stride_kv=[2, 2, 2], padding_q=[1, 1, 1], padding_kv=[1, 1, 1], attn_drop_p=0.1, attn_proj_drop_p=0.1, drop_path_p=0.1, drop_p=0.1):\n super().__init__()\n self.cls_token = nn.Parameter(data=torch.zeros(size=(1, 1, emb_dim[2])), requires_grad=True)\n self.conv_token_emb1 = ConvolutionalTokenEmbedding(in_channel, emb_dim[0], kernel_size[0], stride[0], padding[0])\n self.transformer_block1 = TransformerBlock(None, n_layers[0], emb_dim[0], mlp_expansion, num_heads[0], attn_kernel_size, stride_q[0], stride_kv[0], padding_q[0], padding_kv[0], attn_drop_p, attn_proj_drop_p, drop_path_p, drop_p)\n self.conv_token_emb2 = ConvolutionalTokenEmbedding(emb_dim[0], emb_dim[1], kernel_size[1], stride[1], padding[1])\n self.transformer_block2 = TransformerBlock(None, n_layers[1], emb_dim[1], mlp_expansion, num_heads[1], attn_kernel_size, stride_q[1], stride_kv[1], padding_q[1], padding_kv[1], attn_drop_p, attn_proj_drop_p, drop_path_p, drop_p)\n self.conv_token_emb3 = ConvolutionalTokenEmbedding(emb_dim[1], emb_dim[2], kernel_size[2], stride[2], padding[2])\n self.transformer_block3 = TransformerBlock(self.cls_token, n_layers[2], emb_dim[2], mlp_expansion, num_heads[2], attn_kernel_size, stride_q[2], stride_kv[2], padding_q[2], padding_kv[2], attn_drop_p, attn_proj_drop_p, drop_path_p, drop_p)\n self.head = nn.Linear(in_features=emb_dim[2], out_features=num_classes)\n self.pos_drop = nn.Dropout(p=drop_p)\n nn.init.trunc_normal_(self.cls_token, std=0.02)\n self.apply(self._init_weights)\n def _init_weights(self, module):\n if isinstance(module, nn.Linear):\n nn.init.trunc_normal_(module.weight, std=0.02)\n if module.bias is not None:\n nn.init.constant_(module.bias, 0)\n elif isinstance(module, nn.LayerNorm):\n nn.init.constant_(module.weight, 1.0)\n nn.init.constant_(module.bias, 0.0)\n def forward_features(self, x):\n x, H, W = self.conv_token_emb1(x)\n x = self.pos_drop(x)\n x = self.transformer_block1(x, H, W)\n B, N, C = x.shape\n x = x.permute(0, 2, 1).reshape(B, C, H, W)\n x, H, W = self.conv_token_emb2(x)\n x = self.pos_drop(x)\n x = self.transformer_block2(x, H, W)\n B, N, C = x.shape\n x = x.permute(0, 2, 1).reshape(B, C, H, W)\n x, H, W = self.conv_token_emb3(x)\n cls_token = self.cls_token.expand(B, -1, -1)\n x = torch.cat([cls_token, x], dim=1)\n x = self.pos_drop(x)\n x = self.transformer_block3(x, H, W)\n return x\n def forward(self, x):\n x = self.forward_features(x)\n cls_output = x[:, 0]\n return self.head(cls_output)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:29:44.973989Z","iopub.execute_input":"2025-10-13T16:29:44.974638Z","iopub.status.idle":"2025-10-13T16:29:45.000233Z","shell.execute_reply.started":"2025-10-13T16:29:44.97461Z","shell.execute_reply":"2025-10-13T16:29:44.999583Z"}},"outputs":[],"execution_count":73},{"cell_type":"code","source":"\n\nclass AneurysmROIDataset(Dataset):\n def __init__(self, roi_dir, radiomics_df, max_slices=64, target_size=(40, 40)):\n self.roi_dir = roi_dir\n self.radiomics_df = radiomics_df\n self.max_slices = max_slices\n self.target_size = target_size\n self.samples = self._prepare_samples()\n\n def _prepare_samples(self):\n samples = []\n for _, row in self.radiomics_df.iterrows():\n series_uid = str(row['SeriesInstanceUID'])\n location = str(row['Location'])\n location_safe = location.replace(\" \", \"_\").replace(\"/\", \"_\")\n roi_image_path = os.path.join(\n self.roi_dir, location_safe, \n f\"{series_uid}_ROI_{location_safe}_image.nii.gz\"\n )\n if os.path.exists(roi_image_path):\n samples.append({\n 'roi_path': roi_image_path,\n 'series_uid': series_uid,\n 'location': location,\n 'row_idx': row.name\n })\n else:\n print(f\"[WARN] Missing ROI: {roi_image_path}\")\n return samples\n\n def __len__(self):\n return len(self.samples)\n\n def __getitem__(self, idx):\n sample = self.samples[idx]\n try:\n roi_sitk = sitk.ReadImage(sample['roi_path'])\n roi_np = sitk.GetArrayFromImage(roi_sitk).astype(np.float32)\n mn, mx = roi_np.min(), roi_np.max()\n if mx - mn > 1e-8:\n roi_np = (roi_np - mn) / (mx - mn + 1e-8)\n else:\n roi_np[:] = 0.0\n if roi_np.shape[0] < self.max_slices:\n pad_shape = (self.max_slices - roi_np.shape[0], roi_np.shape[1], roi_np.shape[2])\n roi_np = np.concatenate([roi_np, np.zeros(pad_shape, dtype=np.float32)], axis=0)\n elif roi_np.shape[0] > self.max_slices:\n start = (roi_np.shape[0] - self.max_slices) // 2\n roi_np = roi_np[start:start + self.max_slices]\n slice_2d = roi_np[self.max_slices // 2]\n if slice_2d.shape != self.target_size:\n new_slice = np.zeros(self.target_size, dtype=slice_2d.dtype)\n h, w = min(self.target_size[0], slice_2d.shape[0]), min(self.target_size[1], slice_2d.shape[1])\n new_slice[:h, :w] = slice_2d[:h, :w]\n slice_2d = new_slice\n\n if slice_2d.shape != self.target_size:\n print(f\"WARNING: ROI slice at idx {idx} has shape {slice_2d.shape}, resizing to {self.target_size}.\")\n slice_2d = slice_2d[:self.target_size[0], :self.target_size[1]]\n slice_2d = torch.tensor(slice_2d).unsqueeze(0).float()\n meta = {\n 'series_uid': sample['series_uid'],\n 'location': sample['location'],\n 'row_idx': sample['row_idx']\n }\n return {'image': slice_2d, 'meta': meta}\n except Exception as e:\n print(f\"[AneurysmROIDataset] Skipping roi {idx} ({sample['roi_path']}): {e}\")\n return None\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:29:52.424357Z","iopub.execute_input":"2025-10-13T16:29:52.424967Z","iopub.status.idle":"2025-10-13T16:29:52.435482Z","shell.execute_reply.started":"2025-10-13T16:29:52.42494Z","shell.execute_reply":"2025-10-13T16:29:52.43475Z"}},"outputs":[],"execution_count":74},{"cell_type":"code","source":"class DeepFeatureExtractor:\n def __init__(self, model_path=None):\n self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n self.model = ConvolutionalVisionTransformer().to(self.device)\n if model_path and os.path.exists(model_path):\n self.model.load_state_dict(torch.load(model_path, map_location=self.device))\n self.model.eval()\n def extract_features(self, dataloader):\n features_list = []\n metadata_list = []\n with torch.no_grad():\n for batch in tqdm(dataloader, desc=\"Extracting Deep Features\"):\n images = batch['image'].to(self.device)\n features = self.model(images)\n features_list.append(features.cpu().numpy())\n metadata_list.extend([\n {\n 'series_uid': batch['meta'][i]['series_uid'],\n 'location': batch['meta'][i]['location'],\n 'row_idx': batch['meta'][i]['row_idx']\n }\n for i in range(len(batch['meta']))\n])\n\n all_features = np.concatenate(features_list, axis=0)\n return all_features, metadata_list\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:29:58.983651Z","iopub.execute_input":"2025-10-13T16:29:58.98441Z","iopub.status.idle":"2025-10-13T16:29:58.9908Z","shell.execute_reply.started":"2025-10-13T16:29:58.984384Z","shell.execute_reply":"2025-10-13T16:29:58.990052Z"}},"outputs":[],"execution_count":75},{"cell_type":"code","source":"print(\"Number of ROIs in dataset:\", len(roi_dataset))\nprint(\"Radiomics rows:\", len(radiomics_df))\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:30:02.930504Z","iopub.execute_input":"2025-10-13T16:30:02.931205Z","iopub.status.idle":"2025-10-13T16:30:02.935321Z","shell.execute_reply.started":"2025-10-13T16:30:02.931177Z","shell.execute_reply":"2025-10-13T16:30:02.934682Z"}},"outputs":[{"name":"stdout","text":"Number of ROIs in dataset: 140\nRadiomics rows: 140\n","output_type":"stream"}],"execution_count":76},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\nradiomics_df = pd.read_csv('/kaggle/working/radiomics_features_per_ROI_with_modality.csv')\n\n# Add debug: print info about all ROI image files expected\nprint(\"Number of radiomics rows:\", len(radiomics_df))\n\nroi_dataset = AneurysmROIDataset(\n roi_dir='/kaggle/working/roi_extracted',\n radiomics_df=radiomics_df,\n max_slices=64,\n target_size=(40, 40)\n)\n\n# Debug per sample: Check loading and shape for all in the dataset\nprint(\"\\n==== DEBUG: Checking ROI dataset entries ====\")\ndataset_ok = 0\ndataset_failed = 0\nfor i in range(len(roi_dataset)):\n try:\n sample = roi_dataset[i]\n img_shape = sample['image'].shape if hasattr(sample['image'], 'shape') else 'NO_SHAPE'\n print(f\"[{i}] Loaded: shape={img_shape}, SeriesUID={sample['meta']['series_uid']}, Location={sample['meta']['location']}\")\n dataset_ok += 1\n except Exception as e:\n print(f\"[{i}] ERROR loading ROI: {e}\")\n dataset_failed += 1\nprint(f\"TOTAL: {dataset_ok} loaded, {dataset_failed} failed\\n\")\nprint(\"Proceeding to DataLoader (DeepFeatureExtractor step)\")\n\nfrom torch.utils.data import DataLoader\n\ndef custom_collate(batch):\n batch = [b for b in batch if b is not None]\n # This stacks images, and keeps meta-data as a list of dicts per sample\n return {\n 'image': torch.stack([b['image'] for b in batch]),\n 'meta': [b['meta'] for b in batch]\n }\n\ndataloader = DataLoader(\n roi_dataset,\n batch_size=8,\n shuffle=False,\n num_workers=2,\n collate_fn=custom_collate\n)\n\nfeature_extractor = DeepFeatureExtractor()\ntry:\n deep_features, metadata = feature_extractor.extract_features(dataloader)\n print(f\"\\nExtracted {deep_features.shape[0]} deep features\")\nexcept Exception as e:\n print(\"\\n==== ERROR IN DEEP FEATURE EXTRACTION ====\")\n print(e)\n print(\"Investigate failed images/tensors above.\")\n\ndeep_feature_cols = [f'deep_feature_{i}' for i in range(deep_features.shape[1])]\ndeep_df = pd.DataFrame(deep_features, columns=deep_feature_cols)\n\nfor i, meta in enumerate(metadata):\n deep_df.loc[i, 'SeriesInstanceUID'] = meta['series_uid']\n deep_df.loc[i, 'Location'] = meta['location']\n deep_df.loc[i, 'row_idx'] = meta['row_idx']\n\nradiomics_features = radiomics_df.select_dtypes(include=[np.number])\nhybrid_features = []\nhybrid_metadata = []\nfor _, deep_row in deep_df.iterrows():\n row_idx = int(deep_row['row_idx'])\n # Add try-except for radiomics row extraction\n try:\n radiomics_row = radiomics_features.iloc[row_idx].values\n except Exception as e:\n print(f\"[{row_idx}] ERROR extracting radiomics row: {e}\")\n continue\n deep_row_features = deep_row[deep_feature_cols].values.astype(float)\n combined_features = np.concatenate([radiomics_row, deep_row_features])\n hybrid_features.append(combined_features)\n hybrid_metadata.append({\n 'SeriesInstanceUID': deep_row['SeriesInstanceUID'],\n 'Location': deep_row['Location']\n })\nhybrid_features = np.array(hybrid_features)\nradiomics_cols = radiomics_features.columns.tolist()\nhybrid_cols = radiomics_cols + deep_feature_cols\nhybrid_df = pd.DataFrame(hybrid_features, columns=hybrid_cols)\nfor i, meta in enumerate(hybrid_metadata):\n hybrid_df.loc[i, 'SeriesInstanceUID'] = meta['SeriesInstanceUID']\n hybrid_df.loc[i, 'Location'] = meta['Location']\nhybrid_df.to_csv('/kaggle/working/hybrid_radiomics_deep_features.csv', index=False)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:30:05.8212Z","iopub.execute_input":"2025-10-13T16:30:05.821991Z","iopub.status.idle":"2025-10-13T16:30:09.254482Z","shell.execute_reply.started":"2025-10-13T16:30:05.821963Z","shell.execute_reply":"2025-10-13T16:30:09.25355Z"}},"outputs":[{"name":"stdout","text":"Number of radiomics rows: 140\n\n==== DEBUG: Checking ROI dataset entries ====\n[0] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11422928060228360802778018026859204182, Location=Anterior_Communicating_Artery\n[1] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11447542941959800581541313722844637822, Location=Anterior_Communicating_Artery\n[2] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11557464859397815362951522785245632020, Location=Anterior_Communicating_Artery\n[3] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11999987145696510072091906561590137848, Location=Anterior_Communicating_Artery\n[4] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12773309706735630359315214846273921394, Location=Anterior_Communicating_Artery\n[5] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12896910506681881306246412668919668702, Location=Anterior_Communicating_Artery\n[6] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.13359737970612926494907045108541390310, Location=Anterior_Communicating_Artery\n[7] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.13789305723712362238118274295587312089, Location=Anterior_Communicating_Artery\n[8] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.16386250344855221757144432829845114733, Location=Anterior_Communicating_Artery\n[9] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.16984390277144742906667579449023180512, Location=Anterior_Communicating_Artery\n[10] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.23047023542526806696555440426928375679, Location=Anterior_Communicating_Artery\n[11] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.23055827202917388669053993133576833763, Location=Anterior_Communicating_Artery\n[12] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.35378146560080702211693278243609271022, Location=Anterior_Communicating_Artery\n[13] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.36928611823925733133253145871406408988, Location=Anterior_Communicating_Artery\n[14] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.39640919070091958876744231048011388614, Location=Anterior_Communicating_Artery\n[15] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.49718418682238683779854914910561017368, Location=Anterior_Communicating_Artery\n[16] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.50241233088534910114736887318508484246, Location=Anterior_Communicating_Artery\n[17] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.53947155422591684879953627516013605305, Location=Anterior_Communicating_Artery\n[18] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.54865110953409154322874363435644372368, Location=Anterior_Communicating_Artery\n[19] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.56109731607412273442907651635753012241, Location=Anterior_Communicating_Artery\n[20] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.56867346585094457716984380929416039466, Location=Anterior_Communicating_Artery\n[21] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.65011208113835286935212080363533579671, Location=Anterior_Communicating_Artery\n[22] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.68356160898101066850726244725552676010, Location=Anterior_Communicating_Artery\n[23] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.69401690945645968072368812538918487252, Location=Anterior_Communicating_Artery\n[24] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.70243202242722756546202582478829903758, Location=Anterior_Communicating_Artery\n[25] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.75294325392457179365040684378207706807, Location=Anterior_Communicating_Artery\n[26] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.80048101091444895066772572129871971243, Location=Anterior_Communicating_Artery\n[27] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.80114244849666367523293067199486077713, Location=Anterior_Communicating_Artery\n[28] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.84955070686251417902923705821409495324, Location=Anterior_Communicating_Artery\n[29] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.87794163393266428648659243169230666286, Location=Anterior_Communicating_Artery\n[30] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763, Location=Anterior_Communicating_Artery\n[31] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11924949819899884502738782576851659426, Location=Basilar_Tip\n[32] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12226380705607315060235896835122737788, Location=Basilar_Tip\n[33] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.15412988336827906186857260013885503248, Location=Basilar_Tip\n[34] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.27693546360513068451517048347207987807, Location=Basilar_Tip\n[35] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.28722601444191262075880952461419085326, Location=Basilar_Tip\n[36] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.36563348911961346172279351080943665664, Location=Basilar_Tip\n[37] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.42092450058597943280470345107435382425, Location=Basilar_Tip\n[38] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.68356160898101066850726244725552676010, Location=Basilar_Tip\n[39] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.75294325392457179365040684378207706807, Location=Basilar_Tip\n[40] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.89990837914171555676446644356114244393, Location=Basilar_Tip\n[41] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.80114244849666367523293067199486077713, Location=Left_Anterior_Cerebral_Artery\n[42] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11140496970152788589837488009637704168, Location=Left_Infraclinoid_Internal_Carotid_Artery\n[43] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12898332622076283462996059479076432725, Location=Left_Infraclinoid_Internal_Carotid_Artery\n[44] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.67256382079119118825371537284628604044, Location=Left_Infraclinoid_Internal_Carotid_Artery\n[45] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.79942836660118710928733936389534291771, Location=Left_Infraclinoid_Internal_Carotid_Artery\n[46] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.92543328866053664733167983708344898988, Location=Left_Infraclinoid_Internal_Carotid_Artery\n[47] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381, Location=Left_Middle_Cerebral_Artery\n[48] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12873050136415197430227722045995986358, Location=Left_Middle_Cerebral_Artery\n[49] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.31897325247898403027455884342546675049, Location=Left_Middle_Cerebral_Artery\n[50] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.77257791208759842602760935296318202703, Location=Left_Middle_Cerebral_Artery\n[51] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.84908441442551598157537604822760711232, Location=Left_Middle_Cerebral_Artery\n[52] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382, Location=Left_Posterior_Communicating_Artery\n[53] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.38245669369430321272819874468980907728, Location=Left_Posterior_Communicating_Artery\n[54] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.66341469849558089736451534296312923277, Location=Left_Posterior_Communicating_Artery\n[55] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.75798029534455454939797323020706657426, Location=Left_Posterior_Communicating_Artery\n[56] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.86822530556046989269633487715061058236, Location=Left_Posterior_Communicating_Artery\n[57] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.98123758735027035609698227781754927939, Location=Left_Posterior_Communicating_Artery\n[58] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11163718560814217911019576488539324434, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[59] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11821446229980500432989393232863242415, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[60] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12888459003897616398890411591973176636, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[61] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.15777485274723969278718374949878560903, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[62] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.16984390277144742906667579449023180512, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[63] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.36516744229109249667702200145077143886, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[64] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.43536331102142701793144520859521601945, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[65] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.47622062519393262272120105951011625928, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[66] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[67] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.54865110953409154322874363435644372368, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[68] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.56479623144539472445940519727300319231, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[69] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.56867346585094457716984380929416039466, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[70] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.79942836660118710928733936389534291771, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[71] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.80461517820710375402982229582943598734, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[72] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.88905360377095450551559885185901908404, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[73] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.90015157820692758596783999454928886688, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[74] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.98123758735027035609698227781754927939, Location=Left_Supraclinoid_Internal_Carotid_Artery\n[75] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382, Location=Other_Posterior_Circulation\n[76] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11639720015527164474926997755882681707, Location=Other_Posterior_Circulation\n[77] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11641438607169452758239778414614826230, Location=Other_Posterior_Circulation\n[78] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11924949819899884502738782576851659426, Location=Other_Posterior_Circulation\n[79] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12180351938456969219537687190067731477, Location=Other_Posterior_Circulation\n[80] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12914952223659958493995413641114579279, Location=Other_Posterior_Circulation\n[81] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.15111820005882064793593034423469604305, Location=Other_Posterior_Circulation\n[82] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.17415277997649872560329721717694101082, Location=Other_Posterior_Circulation\n[83] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.19915189891686122627071348069843885714, Location=Other_Posterior_Circulation\n[84] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.34439485184360273751379923196589017042, Location=Other_Posterior_Circulation\n[85] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.38904475631578710113273863766282479811, Location=Other_Posterior_Circulation\n[86] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.40402571428459178954472078378902050472, Location=Other_Posterior_Circulation\n[87] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.42672154202952548010999212369080894652, Location=Other_Posterior_Circulation\n[88] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.58839417089022860359638460482101293080, Location=Other_Posterior_Circulation\n[89] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.61152918475243358118286003299125054478, Location=Other_Posterior_Circulation\n[90] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.73820261697830420042473892884688067574, Location=Other_Posterior_Circulation\n[91] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.79099213587801933936080747802403048718, Location=Other_Posterior_Circulation\n[92] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.82247540847692847800462620079965863384, Location=Other_Posterior_Circulation\n[93] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.82641698422464356104108563099150990855, Location=Other_Posterior_Circulation\n[94] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381, Location=Right_Anterior_Cerebral_Artery\n[95] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12896910506681881306246412668919668702, Location=Right_Anterior_Cerebral_Artery\n[96] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.16386250344855221757144432829845114733, Location=Right_Anterior_Cerebral_Artery\n[97] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.21004106426734635526381567602936015568, Location=Right_Anterior_Cerebral_Artery\n[98] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.68276712082656957005274595949315894066, Location=Right_Anterior_Cerebral_Artery\n[99] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.88662334466087798807484415780594176763, Location=Right_Anterior_Cerebral_Artery\n[100] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.88739296218460643753583291722714541935, Location=Right_Anterior_Cerebral_Artery\n[101] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12427930128533148989436011949311706348, Location=Right_Infraclinoid_Internal_Carotid_Artery\n[102] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.92773748942952645243074808740855383414, Location=Right_Infraclinoid_Internal_Carotid_Artery\n[103] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.10410600166004340343973545138447283460, Location=Right_Middle_Cerebral_Artery\n[104] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.10929608782694347957516071062422315982, Location=Right_Middle_Cerebral_Artery\n[105] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11504459395565711149380261095223705023, Location=Right_Middle_Cerebral_Artery\n[106] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11624217734793256238140178687655335066, Location=Right_Middle_Cerebral_Artery\n[107] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11938739392606296532297884225608408867, Location=Right_Middle_Cerebral_Artery\n[108] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12709802490782896897031103447163443069, Location=Right_Middle_Cerebral_Artery\n[109] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12792960392435514526913217158720555996, Location=Right_Middle_Cerebral_Artery\n[110] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12812390336793304037901571645929430100, Location=Right_Middle_Cerebral_Artery\n[111] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.24941924992372724575490063788348447936, Location=Right_Middle_Cerebral_Artery\n[112] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.35327124657045713676192746001247576881, Location=Right_Middle_Cerebral_Artery\n[113] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.37086262716517957668471635372810376638, Location=Right_Middle_Cerebral_Artery\n[114] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.39640919070091958876744231048011388614, Location=Right_Middle_Cerebral_Artery\n[115] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.50268462808449401128173812870329002342, Location=Right_Middle_Cerebral_Artery\n[116] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.65654303333996310125136982540737772052, Location=Right_Middle_Cerebral_Artery\n[117] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.77257791208759842602760935296318202703, Location=Right_Middle_Cerebral_Artery\n[118] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.88044882887797890422716086408658477347, Location=Right_Middle_Cerebral_Artery\n[119] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.90168683694094931217787644438845074017, Location=Right_Middle_Cerebral_Artery\n[120] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.97057911327885502714270510313728134927, Location=Right_Middle_Cerebral_Artery\n[121] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.10935907012185032169927418164924236382, Location=Right_Posterior_Communicating_Artery\n[122] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.13359737970612926494907045108541390310, Location=Right_Posterior_Communicating_Artery\n[123] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.38245669369430321272819874468980907728, Location=Right_Posterior_Communicating_Artery\n[124] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430, Location=Right_Posterior_Communicating_Artery\n[125] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.71796601538792777580416179841706319140, Location=Right_Posterior_Communicating_Artery\n[126] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.10035643165968342618460849823699311381, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[127] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.11624217734793256238140178687655335066, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[128] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.12904246053955178641505906243733756576, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[129] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.13128656559176299272467358793386537400, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[130] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.24587963869128721940158079207224095554, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[131] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.38904475631578710113273863766282479811, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[132] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.42933230680553480084056393591634621848, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[133] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.43495968397556043698567120038117641587, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[134] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.52363954882447190271251269039176558430, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[135] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.55520651046049733868642268089599441721, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[136] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.72679260079421518845786364620483278827, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[137] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.88512241250207324783783101806489145581, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[138] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.96218477847514569819859044953648183121, Location=Right_Supraclinoid_Internal_Carotid_Artery\n[139] Loaded: shape=torch.Size([1, 40, 40]), SeriesUID=1.2.826.0.1.3680043.8.498.98133633346919790888527055899070500258, Location=Right_Supraclinoid_Internal_Carotid_Artery\nTOTAL: 140 loaded, 0 failed\n\nProceeding to DataLoader (DeepFeatureExtractor step)\n","output_type":"stream"},{"name":"stderr","text":"Extracting Deep Features: 100%|██████████| 18/18 [00:01<00:00, 15.02it/s]\n","output_type":"stream"},{"name":"stdout","text":"\nExtracted 140 deep features\n","output_type":"stream"}],"execution_count":77},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.feature_selection import SelectFromModel\nfrom sklearn.linear_model import LassoCV\nimport numpy as np\n\n# Select all feature columns (exclude meta)\nmeta_cols = ['SeriesInstanceUID', 'Location', 'Modality']\nfeature_cols = [col for col in hybrid_df.columns if col not in meta_cols]\n\n# Drop any row with NaN in feature_cols or in shape columns\nshape_cols = [\n \"original_shape_Elongation\",\n \"original_shape_Flatness\",\n \"original_shape_LeastAxisLength\",\n \"original_shape_MajorAxisLength\",\n \"original_shape_MinorAxisLength\"\n]\nhybrid_df = hybrid_df.dropna(subset=feature_cols).dropna(subset=shape_cols).reset_index(drop=True)\n\nX = hybrid_df[feature_cols].values\n\n# Create binary labels (1 = aneurysm, 0 = normal) based on known arterial locations\naneurysm_locations = [\n 'Anterior_Communicating_Artery', 'Middle_Cerebral_Artery',\n 'Posterior_Communicating_Artery', 'Basilar_Tip',\n 'Left_Infraclinoid_Internal_Carotid_Artery',\n 'Right_Supraclinoid_Internal_Carotid_Artery'\n]\ny = hybrid_df['Location'].apply(\n lambda x: int(any(loc in str(x) for loc in aneurysm_locations))\n).values\n\n# Check class balance\nprint(\"Label distribution:\", np.unique(y, return_counts=True))\n\n# Confirm and print that no NaN remain\nprint(f\"NaN values in X: {np.isnan(X).sum()}\")\n\n# Scale features\nscaler = StandardScaler()\nX_scaled = scaler.fit_transform(X)\n\n# LASSO feature selection\nlasso_cv = LassoCV(cv=3, random_state=42, max_iter=2000)\nlasso_cv.fit(X_scaled, y)\nlasso_selector = SelectFromModel(lasso_cv, threshold='median', max_features=50)\nlasso_selector.fit(X_scaled, y)\nselected_indices_lasso = lasso_selector.get_support(indices=True)\nselected_features_lasso = [feature_cols[i] for i in selected_indices_lasso]\nprint(f\"LASSO selected {len(selected_features_lasso)} features\")\n\n# Random Forest feature selection\nrf_selector = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)\nrf_selector.fit(X_scaled, y)\nimportances = rf_selector.feature_importances_\nindices = np.argsort(importances)[::-1][:50]\nselected_features_rf = [feature_cols[i] for i in indices]\nprint(f\"Random Forest selected {len(selected_features_rf)} features\")\n\n# Combined unique features\nfinal_selected_features = list(set(selected_features_lasso + selected_features_rf))\nprint(f\"Combined unique features: {len(final_selected_features)}\")\n\n# Final DataFrame: include selected features, meta, and label\navailable_meta_cols = [col for col in meta_cols if col in hybrid_df.columns]\nfinal_df = hybrid_df[final_selected_features + available_meta_cols].copy()\nfinal_df['AneurysmLabel'] = y\n\nfinal_df.to_csv('/kaggle/working/final_hybrid_features_selected.csv', index=False)\nprint(f\"Final feature shape: {final_df.shape}\")\nprint(\"Saved to: /kaggle/working/final_hybrid_features_selected.csv\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:30:37.584053Z","iopub.execute_input":"2025-10-13T16:30:37.58493Z","iopub.status.idle":"2025-10-13T16:31:03.402398Z","shell.execute_reply.started":"2025-10-13T16:30:37.5849Z","shell.execute_reply":"2025-10-13T16:31:03.401666Z"}},"outputs":[{"name":"stdout","text":"Label distribution: (array([0, 1]), array([45, 94]))\nNaN values in X: 0\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.582e-03, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.207e-03, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.195e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.041e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.883e-03, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.394e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.171e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.469e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.867e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.777e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.940e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.737e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.332e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.469e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.069e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.454e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.413e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.842e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.645e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.409e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.155e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.444e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.036e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.251e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.336e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.823e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.289e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.392e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.915e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.734e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.870e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.494e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.833e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.377e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.777e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.013e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.097e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.185e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.289e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.331e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.280e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.994e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.880e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.042e-03, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.444e-03, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.791e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.737e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.491e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.348e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.798e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.759e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.852e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.328e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.133e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.613e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.236e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.422e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.467e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.573e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.023e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.327e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.358e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.286e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.466e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.419e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.295e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.240e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.195e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.454e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.620e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.723e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.779e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.717e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.802e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.883e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.373e-03, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.590e-03, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.946e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.659e-03, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.671e-03, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.801e-03, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.716e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.137e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.876e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.013e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.166e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.794e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.232e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.135e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.871e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.132e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.479e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.555e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.269e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.323e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.746e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.123e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.330e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.580e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.573e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.958e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.935e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.020e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.002e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.969e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.781e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.201e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.572e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.445e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.680e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.183e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.263e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.582e-03, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.207e-03, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.195e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.041e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.883e-03, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.394e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.171e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.469e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.867e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.777e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.940e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.737e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.332e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.469e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.069e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.454e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.413e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.842e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.645e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.409e-02, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.155e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.444e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.036e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.251e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.336e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.823e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.289e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.392e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.915e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.734e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.870e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.494e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.833e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.377e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.777e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.013e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.097e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.185e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.289e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.331e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.280e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.994e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.880e-01, tolerance: 2.296e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.042e-03, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.444e-03, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.791e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.737e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.491e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.348e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.798e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.759e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.852e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.328e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.133e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.613e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.236e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.422e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.467e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.573e-02, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.023e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.327e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.358e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.286e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.466e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.419e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.295e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.240e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.195e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.454e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.620e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.723e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.779e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.717e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.802e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.883e-01, tolerance: 8.925e-04\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.373e-03, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.590e-03, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.946e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.659e-03, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.671e-03, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.801e-03, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.716e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.137e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.876e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.013e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.166e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.794e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.232e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.135e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.871e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.132e-02, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.479e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.555e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.269e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.323e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.746e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.123e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.330e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.580e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.573e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.958e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.935e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.020e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.002e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.969e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.781e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.201e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.572e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.445e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.680e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.183e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.263e-01, tolerance: 2.206e-03\n model = cd_fast.enet_coordinate_descent(\n","output_type":"stream"},{"name":"stdout","text":"LASSO selected 50 features\nRandom Forest selected 50 features\nCombined unique features: 80\nFinal feature shape: (139, 83)\nSaved to: /kaggle/working/final_hybrid_features_selected.csv\n","output_type":"stream"}],"execution_count":78},{"cell_type":"code","source":"hybrid_df=pd.read_csv('/kaggle/input/rsna-intracranial-aneurysm-detection/train.csv')\nhybrid_df.head()\nprint(hybrid_df['Aneurysm Present'].value_counts())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T15:27:57.175888Z","iopub.execute_input":"2025-10-13T15:27:57.176639Z","iopub.status.idle":"2025-10-13T15:27:57.202783Z","shell.execute_reply.started":"2025-10-13T15:27:57.176613Z","shell.execute_reply":"2025-10-13T15:27:57.202029Z"}},"outputs":[{"name":"stdout","text":"Aneurysm Present\n0 2485\n1 1863\nName: count, dtype: int64\n","output_type":"stream"}],"execution_count":56},{"cell_type":"code","source":"from sklearn.impute import SimpleImputer\n\nX = final_df[selected_features_lasso].values\ny = final_df['AneurysmLabel'].values\n\n# Impute missing values for X\nimputer = SimpleImputer(strategy='mean') # or 'median'\nX_imputed = imputer.fit_transform(X)\n\nX_train, X_test, y_train, y_test = train_test_split(\n X_imputed, y, test_size=0.3, random_state=42, stratify=y\n)\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\nrf_model = RandomForestClassifier(\n n_estimators=200, max_depth=10, min_samples_split=5,\n min_samples_leaf=2, random_state=42, n_jobs=-1\n)\nrf_model.fit(X_train_scaled, y_train)\ny_pred = rf_model.predict(X_test_scaled)\ny_pred_proba = rf_model.predict_proba(X_test_scaled)[:, 1]\nprint(\"=== Hybrid Model Performance ===\")\nprint(classification_report(y_test, y_pred))\nprint(f\"AUC Score: {roc_auc_score(y_test, y_pred_proba):.4f}\")\nprint(f\"Confusion Matrix:\\n{confusion_matrix(y_test, y_pred)}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:32:12.142443Z","iopub.execute_input":"2025-10-13T16:32:12.143002Z","iopub.status.idle":"2025-10-13T16:32:23.139063Z","shell.execute_reply.started":"2025-10-13T16:32:12.142979Z","shell.execute_reply":"2025-10-13T16:32:23.138165Z"}},"outputs":[{"name":"stdout","text":"=== Hybrid Model Performance ===\n precision recall f1-score support\n\n 0 0.22 0.14 0.17 14\n 1 0.64 0.75 0.69 28\n\n accuracy 0.55 42\n macro avg 0.43 0.45 0.43 42\nweighted avg 0.50 0.55 0.52 42\n\nAUC Score: 0.5230\nConfusion Matrix:\n[[ 2 12]\n [ 7 21]]\n","output_type":"stream"}],"execution_count":79},{"cell_type":"code","source":"from sklearn.model_selection import StratifiedKFold, cross_val_score\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.ensemble import RandomForestClassifier\n\nX = final_df[selected_features_lasso].values\ny = final_df['AneurysmLabel'].values\n\n# Define a pipeline: scaler + RF model\nclf = make_pipeline(\n StandardScaler(),\n RandomForestClassifier(\n n_estimators=200, max_depth=10, min_samples_split=5,\n min_samples_leaf=2, random_state=42, n_jobs=-1\n )\n)\n\n# 5-fold CV for AUC\ncv_auc = cross_val_score(clf, X, y, cv=5, scoring='roc_auc')\nprint(f\"5-fold cross-validation AUC: {cv_auc.mean():.4f} ± {cv_auc.std():.4f}\")\n\n# If you want average accuracy\ncv_acc = cross_val_score(clf, X, y, cv=5, scoring='accuracy')\nprint(f\"5-fold cross-validation accuracy: {cv_acc.mean():.4f} ± {cv_acc.std():.4f}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-13T16:32:57.316981Z","iopub.execute_input":"2025-10-13T16:32:57.317772Z","iopub.status.idle":"2025-10-13T16:33:01.48941Z","shell.execute_reply.started":"2025-10-13T16:32:57.317743Z","shell.execute_reply":"2025-10-13T16:33:01.488443Z"}},"outputs":[{"name":"stdout","text":"5-fold cross-validation AUC: 0.5533 ± 0.0286\n5-fold cross-validation accuracy: 0.6399 ± 0.0355\n","output_type":"stream"}],"execution_count":80},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}