{
 "cells": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "\u001b[33mWARNING: typer 0.12.3 does not provide the extra 'all'\u001b[0m\u001b[33m\r\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\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.\r\n",
      "bigframes 0.22.0 requires google-cloud-bigquery[bqstorage,pandas]>=3.10.0, but you have google-cloud-bigquery 2.34.4 which is incompatible.\r\n",
      "bigframes 0.22.0 requires google-cloud-storage>=2.0.0, but you have google-cloud-storage 1.44.0 which is incompatible.\r\n",
      "bigframes 0.22.0 requires pandas<2.1.4,>=1.5.0, but you have pandas 2.2.2 which is incompatible.\r\n",
      "dataproc-jupyter-plugin 0.1.79 requires pydantic~=1.10.0, but you have pydantic 2.9.2 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install --quiet /kaggle/input/timm_3d_deps/other/initial/9/pydicom/pydicom/pydicom-2.4.4-py3-none-any.whl\n",
    "%pip install timm_3d --no-index --quiet --find-links=/kaggle/input/timm_3d_deps/other/initial/9/timm_3d/\n",
    "%pip install torchio --no-index --quiet --find-links=/kaggle/input/timm_3d_deps/other/initial/9/torchio/\n",
    "%pip install itk --no-index --quiet --find-links=/kaggle/input/timm_3d_deps/other/initial/9/itk/itk\n",
    "%pip install skorch --no-index --quiet --find-links=/kaggle/input/timm_3d_deps/other/initial/9/skorch/skorch\n",
    "%pip install spacecutter --no-index --quiet --find-links=/kaggle/input/timm_3d_deps/other/initial/9/spacecutter/\n",
    "%pip install open3d --no-index --quiet --find-links=/kaggle/input/timm_3d_deps/other/initial/9/open3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "067bcaa2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:37.304646Z",
     "iopub.status.busy": "2024-10-09T13:34:37.304008Z",
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     "shell.execute_reply": "2024-10-09T13:34:49.555312Z"
    },
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     "end_time": "2024-10-09T13:34:49.558605",
     "exception": false,
     "start_time": "2024-10-09T13:34:37.294473",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
      "[Open3D INFO] WebRTC GUI backend enabled.\n",
      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import open3d as o3d\n",
    "from pydicom import dcmread\n",
    "import math\n",
    "import numpy as np\n",
    "import cv2\n",
    "import copy\n",
    "import numpy as np\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torchio as tio\n",
    "import torch.nn as nn\n",
    "import pydicom\n",
    "import matplotlib.pyplot as plt\n",
    "import glob \n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import timm_3d\n",
    "from spacecutter import *\n",
    "from spacecutter.losses import *\n",
    "from spacecutter.models import *\n",
    "from spacecutter.callbacks import *\n",
    "import glob\n",
    "import os\n",
    "import torch\n",
    "from torch.cuda.amp import autocast\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccec34f2",
   "metadata": {
    "papermill": {
     "duration": 0.00824,
     "end_time": "2024-10-09T13:34:49.575675",
     "exception": false,
     "start_time": "2024-10-09T13:34:49.567435",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Data retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "02e3da89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:49.594181Z",
     "iopub.status.busy": "2024-10-09T13:34:49.593801Z",
     "iopub.status.idle": "2024-10-09T13:34:49.619539Z",
     "shell.execute_reply": "2024-10-09T13:34:49.618515Z"
    },
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     "duration": 0.037322,
     "end_time": "2024-10-09T13:34:49.621470",
     "exception": false,
     "start_time": "2024-10-09T13:34:49.584148",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data retrieved successfully.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>study_id</th>\n",
       "      <th>series_id</th>\n",
       "      <th>series_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44036939</td>\n",
       "      <td>2828203845</td>\n",
       "      <td>Sagittal T1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44036939</td>\n",
       "      <td>3481971518</td>\n",
       "      <td>Axial T2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44036939</td>\n",
       "      <td>3844393089</td>\n",
       "      <td>Sagittal T2/STIR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   study_id   series_id series_description\n",
       "0  44036939  2828203845        Sagittal T1\n",
       "1  44036939  3481971518           Axial T2\n",
       "2  44036939  3844393089   Sagittal T2/STIR"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = \"/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/\"\n",
    "def retrieve_test_data(data_path):\n",
    "    test_df = pd.read_csv(data_path + 'test_series_descriptions.csv')\n",
    "    print(\"Test data retrieved successfully.\")\n",
    "    return test_df\n",
    "\n",
    "retrieve_test_data(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c20ce19",
   "metadata": {
    "papermill": {
     "duration": 0.009285,
     "end_time": "2024-10-09T13:34:49.640183",
     "exception": false,
     "start_time": "2024-10-09T13:34:49.630898",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Retrieve Image Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d99cdd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:49.659966Z",
     "iopub.status.busy": "2024-10-09T13:34:49.659635Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def retrieve_image_paths(base_path, study_id, series_id):\n",
    "    series_dir = os.path.join(base_path, str(study_id), str(series_id))\n",
    "    images = os.listdir(series_dir)\n",
    "    image_paths = [os.path.join(series_dir, img) for img in images]\n",
    "    print(f\"Retrieved image paths for study_id: {study_id}, series_id: {series_id}.\")\n",
    "    return image_paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d12b7edb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:49.687038Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Step 3: Read Study as Point Cloud Data\n",
    "def read_study_as_pcd(dir_path, series_types_dict=None, downsampling_factor=1, img_size=(256, 256)):\n",
    "    pcd_overall = o3d.geometry.PointCloud()\n",
    "\n",
    "    for path in glob.glob(os.path.join(dir_path, \"**/*.dcm\"), recursive=True):\n",
    "        dicom_slice = dcmread(path)\n",
    "\n",
    "        series_id = os.path.basename(os.path.dirname(path))\n",
    "        study_id = os.path.basename(os.path.dirname(os.path.dirname(path)))\n",
    "        if series_types_dict is None or int(series_id) not in series_types_dict:\n",
    "            series_desc = dicom_slice.SeriesDescription\n",
    "        else:\n",
    "            series_desc = series_types_dict[int(series_id)]\n",
    "            series_desc = series_desc.split(\" \")[-1]\n",
    "\n",
    "        x_orig, y_orig = dicom_slice.pixel_array.shape\n",
    "        img = np.expand_dims(cv2.resize(dicom_slice.pixel_array, img_size, interpolation=cv2.INTER_AREA), -1)\n",
    "        x, y, z = np.where(img)\n",
    "        downsampling_factor_iter = max(downsampling_factor, int(math.ceil(len(x) / 6e6)))\n",
    "\n",
    "        index_voxel = np.vstack((x, y, z))[:, ::downsampling_factor_iter]\n",
    "        grid_index_array = index_voxel.T\n",
    "        pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(grid_index_array.astype(np.float64)))\n",
    "\n",
    "        vals = np.expand_dims(img[x, y, z][::downsampling_factor_iter], -1)\n",
    "        if series_desc == \"T1\":\n",
    "            vals = np.pad(vals, ((0, 0), (0, 2)))\n",
    "        elif series_desc == \"T2\":\n",
    "            vals = np.pad(vals, ((0, 0), (1, 1)))\n",
    "        elif series_desc == \"T2/STIR\":\n",
    "            vals = np.pad(vals, ((0, 0), (2, 0)))\n",
    "        else:\n",
    "            raise ValueError(f\"Unknown series desc: {series_desc}\")\n",
    "\n",
    "        pcd.colors = o3d.utility.Vector3dVector(vals.astype(np.float64))\n",
    "        dX, dY = dicom_slice.PixelSpacing\n",
    "        dZ = dicom_slice.SliceThickness\n",
    "\n",
    "        X = np.array(list(dicom_slice.ImageOrientationPatient[:3]) + [0]) * dX\n",
    "        Y = np.array(list(dicom_slice.ImageOrientationPatient[3:]) + [0]) * dY\n",
    "\n",
    "        for z in range(int(dZ)):\n",
    "            pos = list(dicom_slice.ImagePositionPatient)\n",
    "            if series_desc == \"T2\":\n",
    "                pos[-1] += z\n",
    "            else:\n",
    "                pos[0] += z\n",
    "            S = np.array(pos + [1])\n",
    "\n",
    "            transform_matrix = np.array([X, Y, np.zeros(len(X)), S]).T\n",
    "            transform_matrix = transform_matrix @ np.matrix(\n",
    "                [[0, y_orig / img_size[1], 0, 0],\n",
    "                 [x_orig / img_size[0], 0, 0, 0],\n",
    "                 [0, 0, 1, 0],\n",
    "                 [0, 0, 0, 1]]\n",
    "            )\n",
    "            pcd_overall += copy.deepcopy(pcd).transform(transform_matrix)\n",
    "\n",
    "    return pcd_overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4db6a6eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:49.725577Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "def read_study_as_voxel_grid(dir_path, series_type_dict=None, downsampling_factor=1, img_size=(256, 256)):\n",
    "    pcd_overall = read_study_as_pcd(dir_path,\n",
    "                                    series_types_dict=series_type_dict,\n",
    "                                    downsampling_factor=downsampling_factor,\n",
    "                                    img_size=img_size)\n",
    "    box = pcd_overall.get_axis_aligned_bounding_box()\n",
    "\n",
    "    max_b = np.array(box.get_max_bound())\n",
    "    min_b = np.array(box.get_min_bound())\n",
    "\n",
    "    pts = (np.array(pcd_overall.points) - (min_b)) * (\n",
    "                (img_size[0] - 1, img_size[0] - 1, img_size[0] - 1) / (max_b - min_b))\n",
    "    coords = np.round(pts).astype(np.int32)\n",
    "    vals = np.array(pcd_overall.colors, dtype=np.float16)\n",
    "\n",
    "    grid = np.zeros((3, img_size[0], img_size[0], img_size[0]), dtype=np.float16)\n",
    "    indices = coords[:, 0], coords[:, 1], coords[:, 2]\n",
    "\n",
    "    np.maximum.at(grid[0], indices, vals[:, 0])\n",
    "    np.maximum.at(grid[1], indices, vals[:, 1])\n",
    "    np.maximum.at(grid[2], indices, vals[:, 2])\n",
    "    print(\"Study read as voxel grid successfully.\")\n",
    "    return grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e432ab31",
   "metadata": {
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     "duration": 0.008388,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "TestSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "459929ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:49.772556Z",
     "iopub.status.busy": "2024-10-09T13:34:49.772242Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "CONDITIONS = {\n",
    "    \"Sagittal T2/STIR\": [\"Spinal Canal Stenosis\"],\n",
    "    \"Axial T2\": [\"Left Subarticular Stenosis\", \"Right Subarticular Stenosis\"],\n",
    "    \"Sagittal T1\": [\"Left Neural Foraminal Narrowing\", \"Right Neural Foraminal Narrowing\"],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d872c82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:49.796725Z",
     "iopub.status.busy": "2024-10-09T13:34:49.796419Z",
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    },
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     "status": "completed"
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   "outputs": [],
   "source": [
    "class PatientLevelTestset(Dataset):\n",
    "    def __init__(self,\n",
    "                 base_path: str,\n",
    "                 dataframe: pd.DataFrame,\n",
    "                 transform_3d=None):\n",
    "        self.base_path = base_path\n",
    "\n",
    "        self.dataframe = (dataframe[['study_id', \"series_id\", \"series_description\"]]\n",
    "                          .drop_duplicates())\n",
    "\n",
    "        self.subjects = self.dataframe[['study_id']].drop_duplicates().reset_index(drop=True)\n",
    "        self.series_descs = {e[0]: e[1] for e in self.dataframe[[\"series_id\", \"series_description\"]].drop_duplicates().values}\n",
    "\n",
    "        self.transform_3d = transform_3d\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.subjects)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        curr = self.subjects.iloc[index]\n",
    "        study_path = os.path.join(self.base_path, str(curr[\"study_id\"]))\n",
    "\n",
    "        study_images = read_study_as_voxel_grid(study_path, self.series_descs)\n",
    "        if self.transform_3d is not None:\n",
    "            study_images = self.transform_3d(torch.FloatTensor(study_images))  # .data\n",
    "            return study_images.to(torch.half), str(curr[\"study_id\"])\n",
    "\n",
    "        return torch.HalfTensor(study_images), str(curr[\"study_id\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ddb19b",
   "metadata": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Data Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "df1430bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:49.842426Z",
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     "status": "completed"
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   },
   "outputs": [],
   "source": [
    "transform_3d = tio.Compose([\n",
    "    tio.RescaleIntensity([0, 1]),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a493edd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:49.866147Z",
     "iopub.status.busy": "2024-10-09T13:34:49.865834Z",
     "iopub.status.idle": "2024-10-09T13:34:49.871380Z",
     "shell.execute_reply": "2024-10-09T13:34:49.870697Z"
    },
    "papermill": {
     "duration": 0.016757,
     "end_time": "2024-10-09T13:34:49.873240",
     "exception": false,
     "start_time": "2024-10-09T13:34:49.856483",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_subject_level_testset_and_loader(df: pd.DataFrame,\n",
    "                                             transform_3d,\n",
    "                                             base_path: str,\n",
    "                                             batch_size=1,\n",
    "                                             num_workers=0):\n",
    "    testset = PatientLevelTestset(base_path, df, transform_3d=transform_3d)\n",
    "    test_loader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
    "    print(\"Subject level testset and data loader created successfully.\")\n",
    "    return testset, test_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b167f5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:49.891942Z",
     "iopub.status.busy": "2024-10-09T13:34:49.891094Z",
     "iopub.status.idle": "2024-10-09T13:34:49.910099Z",
     "shell.execute_reply": "2024-10-09T13:34:49.909133Z"
    },
    "papermill": {
     "duration": 0.030464,
     "end_time": "2024-10-09T13:34:49.912111",
     "exception": false,
     "start_time": "2024-10-09T13:34:49.881647",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data retrieved successfully.\n",
      "Subject level testset and data loader created successfully.\n"
     ]
    }
   ],
   "source": [
    "data = retrieve_test_data(data_path)\n",
    "dataset, dataloader = create_subject_level_testset_and_loader(data, transform_3d, os.path.join(data_path, \"test_images\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f689193f",
   "metadata": {
    "papermill": {
     "duration": 0.008619,
     "end_time": "2024-10-09T13:34:49.929556",
     "exception": false,
     "start_time": "2024-10-09T13:34:49.920937",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Visulaize Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c1f26a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:34:49.948137Z",
     "iopub.status.busy": "2024-10-09T13:34:49.947836Z",
     "iopub.status.idle": "2024-10-09T13:35:08.909341Z",
     "shell.execute_reply": "2024-10-09T13:35:08.908437Z"
    },
    "papermill": {
     "duration": 18.97567,
     "end_time": "2024-10-09T13:35:08.913854",
     "exception": false,
     "start_time": "2024-10-09T13:34:49.938184",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Study read as voxel grid successfully.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualization completed.\n"
     ]
    }
   ],
   "source": [
    "grid = dataset[0][0]\n",
    "\n",
    "fig, axs = plt.subplots(3, 3)\n",
    "\n",
    "axs[0, 0].imshow(grid[0, 128])\n",
    "axs[1, 0].imshow(grid[1, 128])\n",
    "axs[2, 0].imshow(grid[2, 128])\n",
    "\n",
    "axs[0, 1].imshow(grid[0, :, 128])\n",
    "axs[1, 1].imshow(grid[1, :, 128])\n",
    "axs[2, 1].imshow(grid[2, :, 128])\n",
    "\n",
    "axs[0, 2].imshow(grid[0, :, :, 128])\n",
    "axs[1, 2].imshow(grid[1, :, :, 128])\n",
    "axs[2, 2].imshow(grid[2, :, :, 128])\n",
    "\n",
    "plt.show()\n",
    "print(\"Visualization completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "407b6cb7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:35:08.937546Z",
     "iopub.status.busy": "2024-10-09T13:35:08.937214Z",
     "iopub.status.idle": "2024-10-09T13:35:08.944657Z",
     "shell.execute_reply": "2024-10-09T13:35:08.943805Z"
    },
    "papermill": {
     "duration": 0.021538,
     "end_time": "2024-10-09T13:35:08.946692",
     "exception": false,
     "start_time": "2024-10-09T13:35:08.925154",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f633c75",
   "metadata": {
    "papermill": {
     "duration": 0.014974,
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     "exception": false,
     "start_time": "2024-10-09T13:35:08.958254",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47d190d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:35:09.002857Z",
     "iopub.status.busy": "2024-10-09T13:35:09.002470Z",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "class CNN_Model_3D_Multihead(nn.Module):\n",
    "    def __init__(self,\n",
    "                 backbone=\"efficientnet_lite0\",\n",
    "                 in_chans=1,\n",
    "                 out_classes=5,\n",
    "                 cutpoint_margin=0.15,\n",
    "                 pretrained=False):\n",
    "        super(CNN_Model_3D_Multihead, self).__init__()\n",
    "        self.out_classes = out_classes\n",
    "\n",
    "        self.encoder = timm_3d.create_model(\n",
    "            backbone,\n",
    "            features_only=False,\n",
    "            drop_rate=0,\n",
    "            drop_path_rate=0,\n",
    "            pretrained=pretrained,\n",
    "            in_chans=in_chans,\n",
    "            global_pool=\"max\"\n",
    "        )\n",
    "        if \"efficientnet\" in backbone:\n",
    "            head_in_dim = self.encoder.classifier.in_features\n",
    "            self.encoder.classifier = nn.Sequential(\n",
    "                nn.LayerNorm(head_in_dim),\n",
    "                nn.Dropout(0),\n",
    "            )\n",
    "\n",
    "        elif \"vit\" in backbone:\n",
    "            self.encoder.head.drop = nn.Dropout(0)\n",
    "            head_in_dim = self.encoder.head.fc.in_features\n",
    "            self.encoder.head.fc = nn.Identity()\n",
    "\n",
    "        self.heads = nn.ModuleList(\n",
    "            [nn.Sequential(\n",
    "                nn.Linear(head_in_dim, 1),\n",
    "                LogisticCumulativeLink(3)\n",
    "            ) for i in range(out_classes)]\n",
    "        )\n",
    "\n",
    "        self.ascension_callback = AscensionCallback(margin=cutpoint_margin)\n",
    "\n",
    "    def forward(self, x):\n",
    "        feat = self.encoder(x)\n",
    "        return torch.swapaxes(torch.stack([head(feat) for head in self.heads]), 0, 1)\n",
    "\n",
    "    def _ascension_callback(self):\n",
    "        for head in self.heads:\n",
    "            self.ascension_callback.clip(head[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df46381e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:35:09.041200Z",
     "iopub.status.busy": "2024-10-09T13:35:09.040405Z",
     "iopub.status.idle": "2024-10-09T13:35:12.058098Z",
     "shell.execute_reply": "2024-10-09T13:35:12.057062Z"
    },
    "papermill": {
     "duration": 3.031896,
     "end_time": "2024-10-09T13:35:12.060615",
     "exception": false,
     "start_time": "2024-10-09T13:35:09.028719",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /usr/local/src/pytorch/aten/src/ATen/native/TensorShape.cpp:3609.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "/tmp/ipykernel_24/1425751659.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load(\"/kaggle/input/rsna-2024/pytorch/vit_voxel_v2/7/maxvit_rmlp_tiny_rw_256_256_v2_fold_3_32.pt\"))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = CNN_Model_3D_Multihead(backbone=\"maxvit_rmlp_tiny_rw_256\", in_chans=3, out_classes=25).to(device)\n",
    "model.load_state_dict(torch.load(\"/kaggle/input/rsna-2024/pytorch/vit_voxel_v2/7/maxvit_rmlp_tiny_rw_256_256_v2_fold_3_32.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "131747a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:35:12.088932Z",
     "iopub.status.busy": "2024-10-09T13:35:12.088014Z",
     "iopub.status.idle": "2024-10-09T13:35:12.098643Z",
     "shell.execute_reply": "2024-10-09T13:35:12.097807Z"
    },
    "papermill": {
     "duration": 0.027101,
     "end_time": "2024-10-09T13:35:12.100941",
     "exception": false,
     "start_time": "2024-10-09T13:35:12.073840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['left_neural_foraminal_narrowing',\n",
       " 'left_subarticular_stenosis',\n",
       " 'right_neural_foraminal_narrowing',\n",
       " 'right_subarticular_stenosis',\n",
       " 'spinal_canal_stenosis']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CONDITIONS = {\n",
    "    \"Sagittal T2/STIR\": [\"spinal_canal_stenosis\"],\n",
    "    \"Axial T2\": [\"left_subarticular_stenosis\", \"right_subarticular_stenosis\"],\n",
    "    \"Sagittal T1\": [\"left_neural_foraminal_narrowing\", \"right_neural_foraminal_narrowing\"],\n",
    "}\n",
    "\n",
    "ALL_CONDITIONS = sorted([\"spinal_canal_stenosis\", \"left_subarticular_stenosis\", \"right_subarticular_stenosis\", \"left_neural_foraminal_narrowing\", \"right_neural_foraminal_narrowing\"])\n",
    "LEVELS = [\"l1_l2\", \"l2_l3\", \"l3_l4\", \"l4_l5\", \"l5_s1\"]\n",
    "\n",
    "results_df = pd.DataFrame({\"row_id\":[], \"normal_mild\": [], \"moderate\": [], \"severe\": []})\n",
    "\n",
    "ALL_CONDITIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9307ff",
   "metadata": {
    "papermill": {
     "duration": 0.012693,
     "end_time": "2024-10-09T13:35:12.127730",
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     "start_time": "2024-10-09T13:35:12.115037",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Pre populate results df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14ea4ba7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:35:12.153267Z",
     "iopub.status.busy": "2024-10-09T13:35:12.152953Z",
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    },
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     "start_time": "2024-10-09T13:35:12.140392",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "study_ids = glob.glob(\"/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/test_images/*\")\n",
    "study_ids = [os.path.basename(e) for e in study_ids]\n",
    "\n",
    "results_df = pd.DataFrame({\"row_id\":[], \"normal_mild\": [], \"moderate\": [], \"severe\": []})\n",
    "for study_id in study_ids:\n",
    "    for condition in ALL_CONDITIONS:\n",
    "        for level in LEVELS:\n",
    "            row_id = f\"{study_id}_{condition}_{level}\"\n",
    "            results_df = results_df._append({\"row_id\": row_id, \"normal_mild\": 1/3, \"moderate\": 1/3, \"severe\": 1/3}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "15599327",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:35:12.213233Z",
     "iopub.status.busy": "2024-10-09T13:35:12.212935Z",
     "iopub.status.idle": "2024-10-09T13:35:12.222874Z",
     "shell.execute_reply": "2024-10-09T13:35:12.221819Z"
    },
    "papermill": {
     "duration": 0.025391,
     "end_time": "2024-10-09T13:35:12.224974",
     "exception": false,
     "start_time": "2024-10-09T13:35:12.199583",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>study_id</th>\n",
       "      <th>series_id</th>\n",
       "      <th>series_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44036939</td>\n",
       "      <td>2828203845</td>\n",
       "      <td>Sagittal T1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44036939</td>\n",
       "      <td>3481971518</td>\n",
       "      <td>Axial T2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44036939</td>\n",
       "      <td>3844393089</td>\n",
       "      <td>Sagittal T2/STIR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   study_id   series_id series_description\n",
       "0  44036939  2828203845        Sagittal T1\n",
       "1  44036939  3481971518           Axial T2\n",
       "2  44036939  3844393089   Sagittal T2/STIR"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d5dba0",
   "metadata": {
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     "duration": 0.012089,
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     "start_time": "2024-10-09T13:35:12.236406",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Make Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fc74178f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:35:12.272598Z",
     "iopub.status.busy": "2024-10-09T13:35:12.272284Z",
     "iopub.status.idle": "2024-10-09T13:35:30.654917Z",
     "shell.execute_reply": "2024-10-09T13:35:30.653973Z"
    },
    "papermill": {
     "duration": 18.397053,
     "end_time": "2024-10-09T13:35:30.657097",
     "exception": false,
     "start_time": "2024-10-09T13:35:12.260044",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Study read as voxel grid successfully.\n",
      "--- 18.374985456466675 seconds ---\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "with torch.no_grad():\n",
    "    # Update autocast usage as per the new syntax\n",
    "    with torch.amp.autocast('cuda', dtype=torch.float16):\n",
    "        model.eval()\n",
    "\n",
    "        for images, study_id in dataloader:\n",
    "            output = model(images.to(device))\n",
    "            for i, batch_out in enumerate(output):\n",
    "                batch_out = output.cpu().numpy()[i]\n",
    "                for index, level in enumerate(batch_out):\n",
    "                    row_id = f\"{study_id[i]}_{ALL_CONDITIONS[index // 5]}_{LEVELS[index % 5]}\"\n",
    "                    results_df.loc[results_df.row_id == row_id, 'normal_mild'] = level[0]\n",
    "                    results_df.loc[results_df.row_id == row_id, 'moderate'] = level[1]\n",
    "                    results_df.loc[results_df.row_id == row_id, 'severe'] = level[2]\n",
    "                \n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "03d280a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:35:30.682781Z",
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    },
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     "duration": 0.029463,
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     "start_time": "2024-10-09T13:35:30.669081",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th>moderate</th>\n",
       "      <th>severe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44036939_left_neural_foraminal_narrowing_l1_l2</td>\n",
       "      <td>0.416031</td>\n",
       "      <td>0.406802</td>\n",
       "      <td>0.177167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44036939_left_neural_foraminal_narrowing_l2_l3</td>\n",
       "      <td>0.551157</td>\n",
       "      <td>0.380298</td>\n",
       "      <td>0.068545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44036939_left_neural_foraminal_narrowing_l3_l4</td>\n",
       "      <td>0.303115</td>\n",
       "      <td>0.541064</td>\n",
       "      <td>0.155821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44036939_left_neural_foraminal_narrowing_l4_l5</td>\n",
       "      <td>0.146874</td>\n",
       "      <td>0.501656</td>\n",
       "      <td>0.351471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44036939_left_neural_foraminal_narrowing_l5_s1</td>\n",
       "      <td>0.050672</td>\n",
       "      <td>0.230981</td>\n",
       "      <td>0.718347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>44036939_left_subarticular_stenosis_l1_l2</td>\n",
       "      <td>0.593061</td>\n",
       "      <td>0.324840</td>\n",
       "      <td>0.082099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>44036939_left_subarticular_stenosis_l2_l3</td>\n",
       "      <td>0.238710</td>\n",
       "      <td>0.521463</td>\n",
       "      <td>0.239828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44036939_left_subarticular_stenosis_l3_l4</td>\n",
       "      <td>0.209394</td>\n",
       "      <td>0.538259</td>\n",
       "      <td>0.252347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>44036939_left_subarticular_stenosis_l4_l5</td>\n",
       "      <td>0.151021</td>\n",
       "      <td>0.490411</td>\n",
       "      <td>0.358567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>44036939_left_subarticular_stenosis_l5_s1</td>\n",
       "      <td>0.050627</td>\n",
       "      <td>0.254834</td>\n",
       "      <td>0.694538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>44036939_right_neural_foraminal_narrowing_l1_l2</td>\n",
       "      <td>0.299781</td>\n",
       "      <td>0.433844</td>\n",
       "      <td>0.266375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>44036939_right_neural_foraminal_narrowing_l2_l3</td>\n",
       "      <td>0.555560</td>\n",
       "      <td>0.377126</td>\n",
       "      <td>0.067313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>44036939_right_neural_foraminal_narrowing_l3_l4</td>\n",
       "      <td>0.417631</td>\n",
       "      <td>0.479959</td>\n",
       "      <td>0.102410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>44036939_right_neural_foraminal_narrowing_l4_l5</td>\n",
       "      <td>0.257402</td>\n",
       "      <td>0.526822</td>\n",
       "      <td>0.215776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>44036939_right_neural_foraminal_narrowing_l5_s1</td>\n",
       "      <td>0.029474</td>\n",
       "      <td>0.146124</td>\n",
       "      <td>0.824402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>44036939_right_subarticular_stenosis_l1_l2</td>\n",
       "      <td>0.428498</td>\n",
       "      <td>0.425879</td>\n",
       "      <td>0.145623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>44036939_right_subarticular_stenosis_l2_l3</td>\n",
       "      <td>0.307460</td>\n",
       "      <td>0.508083</td>\n",
       "      <td>0.184457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>44036939_right_subarticular_stenosis_l3_l4</td>\n",
       "      <td>0.421222</td>\n",
       "      <td>0.468460</td>\n",
       "      <td>0.110319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>44036939_right_subarticular_stenosis_l4_l5</td>\n",
       "      <td>0.201217</td>\n",
       "      <td>0.514768</td>\n",
       "      <td>0.284015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>44036939_right_subarticular_stenosis_l5_s1</td>\n",
       "      <td>0.045344</td>\n",
       "      <td>0.232111</td>\n",
       "      <td>0.722546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>44036939_spinal_canal_stenosis_l1_l2</td>\n",
       "      <td>0.764061</td>\n",
       "      <td>0.196065</td>\n",
       "      <td>0.039874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>44036939_spinal_canal_stenosis_l2_l3</td>\n",
       "      <td>0.478047</td>\n",
       "      <td>0.422474</td>\n",
       "      <td>0.099478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>44036939_spinal_canal_stenosis_l3_l4</td>\n",
       "      <td>0.729890</td>\n",
       "      <td>0.236372</td>\n",
       "      <td>0.033738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>44036939_spinal_canal_stenosis_l4_l5</td>\n",
       "      <td>0.505304</td>\n",
       "      <td>0.401123</td>\n",
       "      <td>0.093573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>44036939_spinal_canal_stenosis_l5_s1</td>\n",
       "      <td>0.744213</td>\n",
       "      <td>0.197960</td>\n",
       "      <td>0.057827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             row_id  normal_mild  moderate  \\\n",
       "0    44036939_left_neural_foraminal_narrowing_l1_l2     0.416031  0.406802   \n",
       "1    44036939_left_neural_foraminal_narrowing_l2_l3     0.551157  0.380298   \n",
       "2    44036939_left_neural_foraminal_narrowing_l3_l4     0.303115  0.541064   \n",
       "3    44036939_left_neural_foraminal_narrowing_l4_l5     0.146874  0.501656   \n",
       "4    44036939_left_neural_foraminal_narrowing_l5_s1     0.050672  0.230981   \n",
       "5         44036939_left_subarticular_stenosis_l1_l2     0.593061  0.324840   \n",
       "6         44036939_left_subarticular_stenosis_l2_l3     0.238710  0.521463   \n",
       "7         44036939_left_subarticular_stenosis_l3_l4     0.209394  0.538259   \n",
       "8         44036939_left_subarticular_stenosis_l4_l5     0.151021  0.490411   \n",
       "9         44036939_left_subarticular_stenosis_l5_s1     0.050627  0.254834   \n",
       "10  44036939_right_neural_foraminal_narrowing_l1_l2     0.299781  0.433844   \n",
       "11  44036939_right_neural_foraminal_narrowing_l2_l3     0.555560  0.377126   \n",
       "12  44036939_right_neural_foraminal_narrowing_l3_l4     0.417631  0.479959   \n",
       "13  44036939_right_neural_foraminal_narrowing_l4_l5     0.257402  0.526822   \n",
       "14  44036939_right_neural_foraminal_narrowing_l5_s1     0.029474  0.146124   \n",
       "15       44036939_right_subarticular_stenosis_l1_l2     0.428498  0.425879   \n",
       "16       44036939_right_subarticular_stenosis_l2_l3     0.307460  0.508083   \n",
       "17       44036939_right_subarticular_stenosis_l3_l4     0.421222  0.468460   \n",
       "18       44036939_right_subarticular_stenosis_l4_l5     0.201217  0.514768   \n",
       "19       44036939_right_subarticular_stenosis_l5_s1     0.045344  0.232111   \n",
       "20             44036939_spinal_canal_stenosis_l1_l2     0.764061  0.196065   \n",
       "21             44036939_spinal_canal_stenosis_l2_l3     0.478047  0.422474   \n",
       "22             44036939_spinal_canal_stenosis_l3_l4     0.729890  0.236372   \n",
       "23             44036939_spinal_canal_stenosis_l4_l5     0.505304  0.401123   \n",
       "24             44036939_spinal_canal_stenosis_l5_s1     0.744213  0.197960   \n",
       "\n",
       "      severe  \n",
       "0   0.177167  \n",
       "1   0.068545  \n",
       "2   0.155821  \n",
       "3   0.351471  \n",
       "4   0.718347  \n",
       "5   0.082099  \n",
       "6   0.239828  \n",
       "7   0.252347  \n",
       "8   0.358567  \n",
       "9   0.694538  \n",
       "10  0.266375  \n",
       "11  0.067313  \n",
       "12  0.102410  \n",
       "13  0.215776  \n",
       "14  0.824402  \n",
       "15  0.145623  \n",
       "16  0.184457  \n",
       "17  0.110319  \n",
       "18  0.284015  \n",
       "19  0.722546  \n",
       "20  0.039874  \n",
       "21  0.099478  \n",
       "22  0.033738  \n",
       "23  0.093573  \n",
       "24  0.057827  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "18f77051",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-09T13:35:30.724127Z",
     "iopub.status.busy": "2024-10-09T13:35:30.723841Z",
     "iopub.status.idle": "2024-10-09T13:35:30.731433Z",
     "shell.execute_reply": "2024-10-09T13:35:30.730373Z"
    },
    "papermill": {
     "duration": 0.022227,
     "end_time": "2024-10-09T13:35:30.733385",
     "exception": false,
     "start_time": "2024-10-09T13:35:30.711158",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submission file created successfully.\n"
     ]
    }
   ],
   "source": [
    "results_df.to_csv('submission.csv', index=False)\n",
    "print(\"Submission file created successfully.\")"
   ]
  }
 ],
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