Feb28_2024.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f31ffdc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as  pd\n",
    "import nibabel as nb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import glob\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sktree import ObliqueRandomForestClassifier, PatchObliqueRandomForestClassifier\n",
    "from tqdm import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4edf30a4",
   "metadata": {},
   "outputs": [
    {
     "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Dataset-ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sub-ABIDE1050339</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>MALE</td>\n",
       "      <td>ABIDE</td>\n",
       "      <td>50339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sub-ABIDE1050701</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>MALE</td>\n",
       "      <td>ABIDE</td>\n",
       "      <td>50701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sub-ABIDE1050445</td>\n",
       "      <td>18.1383</td>\n",
       "      <td>MALE</td>\n",
       "      <td>ABIDE</td>\n",
       "      <td>50445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sub-ABIDE1050459</td>\n",
       "      <td>18.1547</td>\n",
       "      <td>MALE</td>\n",
       "      <td>ABIDE</td>\n",
       "      <td>50459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sub-ABIDE1050341</td>\n",
       "      <td>18.2000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ABIDE</td>\n",
       "      <td>50341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID      Age     Sex Dataset Dataset-ID\n",
       "0  sub-ABIDE1050339  18.0000    MALE   ABIDE      50339\n",
       "1  sub-ABIDE1050701  18.0000    MALE   ABIDE      50701\n",
       "2  sub-ABIDE1050445  18.1383    MALE   ABIDE      50445\n",
       "3  sub-ABIDE1050459  18.1547    MALE   ABIDE      50459\n",
       "4  sub-ABIDE1050341  18.2000  FEMALE   ABIDE      50341"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('/cis/home/jdey4/data_MRI/subjects_age_sex_data_MRI.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1543ce6a",
   "metadata": {},
   "outputs": [
    {
     "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>sub</th>\n",
       "      <th>Weighted average (IQR)</th>\n",
       "      <th>Warning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sub-A00008326</td>\n",
       "      <td>83.86</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sub-A00008399</td>\n",
       "      <td>83.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sub-A00010893</td>\n",
       "      <td>78.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sub-A00013809</td>\n",
       "      <td>79.36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sub-A00018030</td>\n",
       "      <td>84.05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sub Weighted average (IQR) Warning\n",
       "0  sub-A00008326                  83.86       0\n",
       "1  sub-A00008399                   83.3       0\n",
       "2  sub-A00010893                   78.3       0\n",
       "3  sub-A00013809                  79.36       0\n",
       "4  sub-A00018030                  84.05       0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_quality = pd.read_csv('/cis/home/jdey4/data_MRI/QC_catreport.csv')\n",
    "df_quality.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361d991e",
   "metadata": {},
   "source": [
    "### Load the mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0cc70a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = nb.load('/cis/home/jdey4/data_MRI/template_cat12/brainmask.nii').get_fdata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6d9bae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mask (Traverse)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5wPn5+VX6e/WVxo0ba+DAgXr33Xf1wAMPSPr1HzHF/0CSqqfW4u+BTp06+aQ9AP9FIAXqqcDAQN1+++1yu91yu926/fbbK3Wf9tNXPJe1wGP27NlVrjE4OFh9+vRxXq+8hSz9+/fXkiVLFBoaqv3796tfv3769ttvq/z6knTFFVdI+nV+q03F/V1WXy9ZskT79+8vt63zzz9fV111lSRp4cKFKioqcoJp69at1atXrxKfN3DgQLlcLknS9OnTK1N+tTn9H1GnL+Tyda0ej0e7du2SJMXHx59zewC8EUiBeuy5555TXl6e8vLyzrprU3lO3xOztP0bZ82aVepddKRf74J05krv0508eVJr166V9Ou819M/Qi/NlVdeqcWLF8vtduvHH39Uv3799N1335X7vNIUB9Kff/5ZGRkZVW7nXBX399KlS0v8WP7bb7919tOsiOJR0P3792vBggXOPMtbb73VCXJnateunbNN08KFCzVt2rQyXyMjI0NvvvlmhWs603fffef8/Zfm9Cknp8//9HWtW7ZsceblFod5AL5DIAVQJd26dVPnzp0lSf/4xz908803a9myZUpPT9eSJUs0bNgw3XfffaWOtklSamqq2rVrp759++qFF17QypUrtXXrVn366aeaM2eOrrjiCucj6Lvvvtv5mLk8SUlJeu+99xQSEqLMzEz1799fe/furdL7vPbaa535lKmpqVVqwxeK5/dmZWUpISFBr7/+ujZt2qR169bpySef1KWXXqqDBw86UxzKM2jQIGcB2/333+8sAipvP9RZs2Y5W1Q9/PDD6tOnj1577TVt2LBB27Zt08cff6yXXnpJV155pdq0aaN33323qm9Z+/btU9++fdWpUyc98cQTWrx4sTZv3qzNmzfrvffe080336yZM2dK+nWh15kjl76stfjvvlmzZurdu3eV3xOAUli7RxSAalfSrUMro7xbh27bts2cd955zjVnPuLi4kxWVlaJt/g0xpgpU6aU+tzTH4MGDTLHjx8/6/VLa7fYBx984Nx6MjY21uzbt6/SfWCMMUOHDjWSTL9+/Sr1vIreOrS086c7efKkueqqq0rto7CwMLNo0aJKtZmcnOzVRpcuXSr0vvbv32+uuOKKCv3djRw58qznF986tE+fPmW+zunfv2U92rdvX+qtQc+11mKxsbFGkhkzZkyF+ghA5TBCCqDKunbtqu3bt+vee+9V69atFRwcrKZNm6pnz5568cUXtWnTJq+7MZ3pT3/6k959912NHj1al112mVq1aqXQ0FCFhobqoosu0k033aRly5Zp8eLFpS7kKcvAgQO1aNEiBQcHKyMjQ/369dMPP/xQ6XZGjRolSVq7dq2z32lNCw4O1vLlyzVjxgx1795dDRo0UFhYmNq0aaN7771XW7duLfOuRyU5czS0oneLio6O1rp167Rs2TIlJyfr4osvVoMGDRQcHKzzzz9fl19+uR5++GGtXbtWr7/+eqVqOt0VV1yhNWvWaOLEierXr5/atGmjxo0bKzg4WFFRUbrqqqs0e/Zsbd++vdTtmnxRa1pamjNdY/To0VV+PwBK5zKGG/ICQFmMMYqLi9OXX36pqVOn6vHHH7ddEmrQH/7wB7322mtKSkrSihUrbJcD1EkEUgCogKVLl+qGG25Qs2bN9P3331fqBgLwX/v27VObNm1UUFCgDRs2sMIeqCZ8ZA8AFTBw4EBdccUV+vnnn52FNKj7UlJSVFBQoGHDhhFGgWrEnZoAoIJmzpypd999V40aNbJdCmqAMUatW7fWlClTdNddd9kuB6jT+MgeAAAAVln7yH7mzJm66KKLFBoaqvj4eG3atMlWKQAAALDIygjpW2+9pTvuuEOzZ89WfHy8pk+frrffflu7du1S8+bNy31+UVGRsrKy1Lhx41LvKAIAAAB7jDE6cuSIYmJiyr01tZVAGh8frx49eujll1+W9GvAbNmype6//35NmDCh3Of/8MMPatmyZXWXCQAAgHOUmZmpCy+8sMxranxR08mTJ5Wenq6JEyc6xwICApSYmKi0tLQSn5Ofn6/8/Hzn6+IM3VvXKkjB1VswAAAAKu2UCrReH6px48blXlvjgfTnn39WYWGhoqKivI5HRUXp66+/LvE5KSkpeuqpp846HqRgBbkIpAAAALXO//8MviLTK/1iH9KJEycqNzfXeWRmZtouCQAAAD5S4yOkzZo1U2BgoHJycryO5+TkKDo6usTnuN1uud3umigPAAAANazGR0hDQkJ06aWXKjU11TlWVFSk1NRUJSQk1HQ5AAAAsMzKnZrGjx+vESNGqHv37urZs6emT5+uY8eOaeTIkTbKAQAAgEVWAunNN9+sn376SZMnT1Z2dra6du2qFStWnLXQCQAAAHWfX9461OPxKCIiQn01iFX2AAAAtdApU6A1WqLc3FyFh4eXea1frLIHAABA3UUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYFWS7AAAoycqs7bZLqDFJMV1tlwAAVjFCCgAAAKsIpAAAALCKQAoAAACrCKQAAACwikVNAKqsPi08qk7V1Y8slgLgLxghBQAAgFUEUgAAAFhFIAUAAIBVzCEFIIn5oHWRr/5OmYsKoLoxQgoAAACrCKQAAACwikAKAAAAqwikAAAAsIpFTUA9wIIlnAtffv+wQApASRghBQAAgFUEUgAAAFhFIAUAAIBVzCEF/BzzQ+FPqvL9yrxToO5jhBQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWLmoBajAVLQNX/P2AxFOA/GCEFAACAVQRSAAAAWEUgBQAAgFXMIQUsYG4oUP3YhB/wH4yQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsYlET4GMsWAL8l6/+/2VxFFA5jJACAADAKgIpAAAArCKQAgAAwCrmkALniDmjAM5U0z8XmLMKf8cIKQAAAKwikAIAAMAqAikAAACsIpACAADAKhY1AWVgwRIAf1BdP6tYLIWawggpAAAArCKQAgAAwCoCKQAAAKxiDinqLeaHAkDZqvpzkrmnqCxGSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVrGoCfUCC5gAoOZU5GcuC59wOkZIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWsagJfo8FSwDgf1j4hNMxQgoAAACrCKQAAACwikAKAAAAq5hDCr/DnFEAqB/O/HnPnNK6ixFSAAAAWEUgBQAAgFUEUgAAAFjl80CakpKiHj16qHHjxmrevLkGDx6sXbt2eV2Tl5enMWPGKDIyUo0aNdLQoUOVk5Pj61IAAADgB1zGGOPLBq+++moNHz5cPXr00KlTp/TYY49px44d2rlzpxo2bChJGj16tJYvX665c+cqIiJCY8eOVUBAgD799NMKvYbH41FERIT6apCCXMG+LB+WsWAJAHAuWPhUe5wyBVqjJcrNzVV4eHiZ1/p8lf2KFSu8vp47d66aN2+u9PR0/f73v1dubq5ee+01LViwQP3795ckzZkzRx06dNCGDRt02WWX+bokAAAA1GLVPoc0NzdXktS0aVNJUnp6ugoKCpSYmOhc0759e7Vq1UppaWkltpGfny+Px+P1AAAAQN1QrYG0qKhI48aNU69evdS5c2dJUnZ2tkJCQtSkSROva6OiopSdnV1iOykpKYqIiHAeLVu2rM6yAQAAUIOqdWP8MWPGaMeOHVq/fv05tTNx4kSNHz/e+drj8RBK6wDmiwIAfK0iv1uYZ1r7VFsgHTt2rJYtW6Z169bpwgsvdI5HR0fr5MmTOnz4sNcoaU5OjqKjo0tsy+12y+12V1epAAAAsMjnH9kbYzR27Fi9//77+uSTTxQbG+t1/tJLL1VwcLBSU1OdY7t27dK+ffuUkJDg63IAAABQy/l8hHTMmDFasGCBlixZosaNGzvzQiMiIhQWFqaIiAjdfffdGj9+vJo2barw8HDdf//9SkhIYIU9AABAPeTzQDpr1ixJUt++fb2Oz5kzR3feeack6a9//asCAgI0dOhQ5efnKykpSX//+999XQoAAAD8gM83xq8JbIzvn1jEBACorVjo5HuV2Rife9kDAADAKgIpAAAArCKQAgAAwKpq3Rgf9QfzQwEA/qyk32PMK605jJACAADAKgIpAAAArCKQAgAAwCoCKQAAAKxiURMqjQVMAID64Mzfdyxyqj6MkAIAAMAqAikAAACsIpACAADAKuaQolzMGQUAgM3zqxMjpAAAALCKQAoAAACrCKQAAACwikAKAAAAq1jUBC8sYAIAoOLYPN83GCEFAACAVQRSAAAAWEUgBQAAgFXMIa3HmC8KAIBvsXl+1TBCCgAAAKsIpAAAALCKQAoAAACrCKQAAACwikVN9QiLmAAAqHlsnl8+RkgBAABgFYEUAAAAVhFIAQAAYBVzSOso5osCAFA7sXn+2RghBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYxaKmOoJFTAAAwF8xQgoAAACrCKQAAACwikAKAAAAq5hDCgAAYNmZa0Hq20b5jJACAADAKgIpAAAArCKQAgAAwCoCKQAAAKxiUZMfYhN8AABQlzBCCgAAAKsIpAAAALCKQAoAAACrCKQAAACwikVNfoBFTAAA1C8l/e6vy3dvYoQUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBUb49cybIIPAABKcmZGqEsb5TNCCgAAAKsIpAAAALCKQAoAAACrmENqGXNGAQBAfccIKQAAAKwikAIAAMAqAikAAACsIpACAADAKhY1AQAA+KGSFkb762b5jJACAADAKgIpAAAArCKQAgAAwCrmkNYgNsEHAAA4GyOkAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsKraA+mzzz4rl8ulcePGOcfy8vI0ZswYRUZGqlGjRho6dKhycnKquxQAAADUQtW6Mf7mzZv1j3/8Q7/73e+8jj/00ENavny53n77bUVERGjs2LEaMmSIPv300+osBwAAoE478yY8STFdrdRRWdU2Qnr06FElJyfr1Vdf1Xnnneccz83N1WuvvaZp06apf//+uvTSSzVnzhx99tln2rBhQ3WVAwAAgFqq2gLpmDFjdN111ykxMdHreHp6ugoKCryOt2/fXq1atVJaWlqJbeXn58vj8Xg9AAAAUDdUy0f2Cxcu1NatW7V58+azzmVnZyskJERNmjTxOh4VFaXs7OwS20tJSdFTTz1VHaUCAADAMp+PkGZmZurBBx/U/PnzFRoa6pM2J06cqNzcXOeRmZnpk3YBAABgn89HSNPT03XgwAFdcsklzrHCwkKtW7dOL7/8slauXKmTJ0/q8OHDXqOkOTk5io6OLrFNt9stt9vt61Kr3ZkTiwEAAHA2nwfSAQMG6IsvvvA6NnLkSLVv316PPvqoWrZsqeDgYKWmpmro0KGSpF27dmnfvn1KSEjwdTkAAACo5XweSBs3bqzOnTt7HWvYsKEiIyOd43fffbfGjx+vpk2bKjw8XPfff78SEhJ02WWX+bocAAAA1HLVug9paf76178qICBAQ4cOVX5+vpKSkvT3v//dRikAAACwzGWMMbaLqCyPx6OIiAj11SAFuYJtl1Mq5pACAACbbG6Mf8oUaI2WKDc3V+Hh4WVey73sAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWWVllDwAAgOpX0gJrmwudSsMIKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAqiDbBdQVK7O22y4BAADALzFCCgAAAKsIpAAAALCKQAoAAACrmEPqI0kxXc86xrxSAABgU0n5pDZihBQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWBdkuoK5YmbXddgkAAAB+iRFSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBV1RJIf/zxR912222KjIxUWFiY4uLitGXLFue8MUaTJ09WixYtFBYWpsTERO3Zs6c6SgEAAEAt5/NAeujQIfXq1UvBwcH66KOPtHPnTr300ks677zznGuef/55zZgxQ7Nnz9bGjRvVsGFDJSUlKS8vz9flAAAAoJYL8nWDzz33nFq2bKk5c+Y4x2JjY50/G2M0ffp0PfHEExo0aJAk6Z///KeioqK0ePFiDR8+3NclAQAAoBbz+QjpBx98oO7du2vYsGFq3ry5unXrpldffdU5n5GRoezsbCUmJjrHIiIiFB8fr7S0tBLbzM/Pl8fj8XoAAACgbvB5IP3uu+80a9YstW3bVitXrtTo0aP1wAMPaN68eZKk7OxsSVJUVJTX86KiopxzZ0pJSVFERITzaNmypa/LBgAAgCU+D6RFRUW65JJL9Mwzz6hbt24aNWqU7rnnHs2ePbvKbU6cOFG5ubnOIzMz04cVAwAAwCafB9IWLVqoY8eOXsc6dOigffv2SZKio6MlSTk5OV7X5OTkOOfO5Ha7FR4e7vUAAABA3eDzQNqrVy/t2rXL69ju3bvVunVrSb8ucIqOjlZqaqpz3uPxaOPGjUpISPB1OQAAAKjlfL7K/qGHHtLll1+uZ555RjfddJM2bdqkV155Ra+88ookyeVyady4cZo6daratm2r2NhYTZo0STExMRo8eLCvywEAAEAt5/NA2qNHD73//vuaOHGi/ud//kexsbGaPn26kpOTnWseeeQRHTt2TKNGjdLhw4fVu3dvrVixQqGhob4uBwAAALWcyxhjbBdRWR6PRxEREeqrQQpyBdsuR5K0Mmu77RIAAADKlRTTtUZe55Qp0BotUW5ubrnrf7iXPQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArAqyXQAAAACqR1JMV9slVAgjpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAq4JsF1BXJMV0PevYyqztNV4HAACov0rKI/6AEVIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVWyMX43O3JyWjfIBAICv+Osm+CVhhBQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFRvj16CSNrBls3wAAFDfMUIKAAAAqwikAAAAsIpACgAAAKuYQwoAAOAHSlqLUlcwQgoAAACrCKQAAACwikAKAAAAqwikAAAAsIpFTZadOUGZjfIBAEB9wwgpAAAArCKQAgAAwCoCKQAAAKxiDikAAEAtU5c3wS8JI6QAAACwikAKAAAAqwikAAAAsIpACgAAAKtY1FTLlDSJmc3yAQBAXcYIKQAAAKwikAIAAMAqnwfSwsJCTZo0SbGxsQoLC9NvfvMb/eUvf5ExxrnGGKPJkyerRYsWCgsLU2Jiovbs2ePrUgAAAOAHfD6H9LnnntOsWbM0b948derUSVu2bNHIkSMVERGhBx54QJL0/PPPa8aMGZo3b55iY2M1adIkJSUlaefOnQoNDfV1SQAAALVafdsI/0w+D6SfffaZBg0apOuuu06SdNFFF+nNN9/Upk2bJP06Ojp9+nQ98cQTGjRokCTpn//8p6KiorR48WINHz7c1yUBAACgFvP5R/aXX365UlNTtXv3bknS559/rvXr1+uaa66RJGVkZCg7O1uJiYnOcyIiIhQfH6+0tLQS28zPz5fH4/F6AAAAoG7w+QjphAkT5PF41L59ewUGBqqwsFBPP/20kpOTJUnZ2dmSpKioKK/nRUVFOefOlJKSoqeeesrXpQIAAKAW8PkI6aJFizR//nwtWLBAW7du1bx58/Tiiy9q3rx5VW5z4sSJys3NdR6ZmZk+rBgAAAA2+XyE9M9//rMmTJjgzAWNi4vT3r17lZKSohEjRig6OlqSlJOToxYtWjjPy8nJUdeuXUts0+12y+12+7pUv3HmRGc2ygcAAHWJz0dIjx8/roAA72YDAwNVVFQkSYqNjVV0dLRSU1Od8x6PRxs3blRCQoKvywEAAEAt5/MR0oEDB+rpp59Wq1at1KlTJ23btk3Tpk3TXXfdJUlyuVwaN26cpk6dqrZt2zrbPsXExGjw4MG+LgcAAAC1nM8D6d/+9jdNmjRJ9913nw4cOKCYmBj98Y9/1OTJk51rHnnkER07dkyjRo3S4cOH1bt3b61YsYI9SAEAAOohlzn9Fkp+wuPxKCIiQn01SEGuYNvl1DjmkAIAULfUxY3xT5kCrdES5ebmKjw8vMxrfT5CiupX0jctIRUAAP9QF8PnufL5oiYAAACgMgikAAAAsIpACgAAAKuYQ1pHsHk+AADwV4yQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsYlFTHcXm+QAA1A5shF8+RkgBAABgFYEUAAAAVhFIAQAAYBVzSAEAAHyE+aJVwwgpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqFjXVI2dOtGajfAAAzg2LmHyDEVIAAABYRSAFAACAVQRSAAAAWMUc0nqspHkvzCsFAKBkzBetPoyQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsYlETvLDQCQCAX7GIqeYwQgoAAACrCKQAAACwikAKAAAAq5hDinKdOYeGOaUAAH/G3NDahxFSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVLGpCpVVkMjgLnwAAtQWLmGo/RkgBAABgFYEUAAAAVhFIAQAAYBVzSFEtSpqvw7xSAICvMT+0bmCEFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYuaUGPOnHjOIicAQGWwgKnuYoQUAAAAVhFIAQAAYBWBFAAAAFYxhxTWsHk+AKAY80PrN0ZIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWsagJtUpFJrWz8AkA/AsLllAeRkgBAABgFYEUAAAAVhFIAQAAYBVzSOF3zpyLxJxSALCH+aHwBUZIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWsagJfo/N9AHAN1igBFsYIQUAAIBVBFIAAABYRSAFAACAVcwhRb3APFMAdRlzP+HvGCEFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFjFoibg/ytpUQALnQBUBIuKgHPDCCkAAACsIpACAADAKgIpAAAArCKQAgAAwCoWNQFl4A5PQP3C4iTADkZIAQAAYBWBFAAAAFZVOpCuW7dOAwcOVExMjFwulxYvXux13hijyZMnq0WLFgoLC1NiYqL27Nnjdc3BgweVnJys8PBwNWnSRHfffbeOHj16Tm8EAAAA/qnSc0iPHTumLl266K677tKQIUPOOv/8889rxowZmjdvnmJjYzVp0iQlJSVp586dCg0NlSQlJydr//79WrVqlQoKCjRy5EiNGjVKCxYsOPd3BNQw5pkC9jH3E/BvLmOMqfKTXS69//77Gjx4sKRfR0djYmL08MMP609/+pMkKTc3V1FRUZo7d66GDx+ur776Sh07dtTmzZvVvXt3SdKKFSt07bXX6ocfflBMTEy5r+vxeBQREaG+GqQgV3BVywdqDIEUqF4EUqD2OWUKtEZLlJubq/Dw8DKv9ekc0oyMDGVnZysxMdE5FhERofj4eKWlpUmS0tLS1KRJEyeMSlJiYqICAgK0cePGEtvNz8+Xx+PxegAAAKBu8Gkgzc7OliRFRUV5HY+KinLOZWdnq3nz5l7ng4KC1LRpU+eaM6WkpCgiIsJ5tGzZ0pdlAwAAwCK/WGU/ceJE5ebmOo/MzEzbJQEAAMBHfLoxfnR0tCQpJydHLVq0cI7n5OSoa9euzjUHDhzwet6pU6d08OBB5/lncrvdcrvdviwVqFEsfAIqjvmgQP3j0xHS2NhYRUdHKzU11Tnm8Xi0ceNGJSQkSJISEhJ0+PBhpaenO9d88sknKioqUnx8vC/LAQAAgB+o9Ajp0aNH9c033zhfZ2RkaPv27WratKlatWqlcePGaerUqWrbtq2z7VNMTIyzEr9Dhw66+uqrdc8992j27NkqKCjQ2LFjNXz48AqtsAcAAEDdUulAumXLFvXr18/5evz48ZKkESNGaO7cuXrkkUd07NgxjRo1SocPH1bv3r21YsUKZw9SSZo/f77Gjh2rAQMGKCAgQEOHDtWMGTN88HYAAADgb85pH1Jb2IcU9RXzTFGbMfcTwOms7UMKAAAAVBaBFAAAAFYRSAEAAGAVgRQAAABW+XRjfADVq6qLRlgMhcpgcRKAmsYIKQAAAKwikAIAAMAqAikAAACsYg4pUA9UZU4g8079D3M/AfgrRkgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFaxqAlAiapzgUx9WjDFQiMAKB8jpAAAALCKQAoAAACrCKQAAACwijmkAGoc8yoBAKdjhBQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGBVkO0CqsIYI0k6pQLJWC4GAAAAZzmlAkn/zW1l8ctAeuTIEUnSen1ouRIAAACU5ciRI4qIiCjzGpepSGytZYqKipSVlSVjjFq1aqXMzEyFh4fbLqtO83g8atmyJX1dzejnmkNf1wz6uebQ1zWDfq44Y4yOHDmimJgYBQSUPUvUL0dIAwICdOGFF8rj8UiSwsPD+aaoIfR1zaCfaw59XTPo55pDX9cM+rliyhsZLcaiJgAAAFhFIAUAAIBVfh1I3W63pkyZIrfbbbuUOo++rhn0c82hr2sG/Vxz6OuaQT9XD79c1AQAAIC6w69HSAEAAOD/CKQAAACwikAKAAAAqwikAAAAsMqvA+nMmTN10UUXKTQ0VPHx8dq0aZPtkvxaSkqKevToocaNG6t58+YaPHiwdu3a5XVNXl6exowZo8jISDVq1EhDhw5VTk6OpYrrhmeffVYul0vjxo1zjtHPvvPjjz/qtttuU2RkpMLCwhQXF6ctW7Y4540xmjx5slq0aKGwsDAlJiZqz549Fiv2T4WFhZo0aZJiY2MVFham3/zmN/rLX/7idQ9r+rry1q1bp4EDByomJkYul0uLFy/2Ol+RPj148KCSk5MVHh6uJk2a6O6779bRo0dr8F34h7L6uqCgQI8++qji4uLUsGFDxcTE6I477lBWVpZXG/R11fltIH3rrbc0fvx4TZkyRVu3blWXLl2UlJSkAwcO2C7Nb61du1ZjxozRhg0btGrVKhUUFOiqq67SsWPHnGseeughLV26VG+//bbWrl2rrKwsDRkyxGLV/m3z5s36xz/+od/97ndex+ln3zh06JB69eql4OBgffTRR9q5c6deeuklnXfeec41zz//vGbMmKHZs2dr48aNatiwoZKSkpSXl2excv/z3HPPadasWXr55Zf11Vdf6bnnntPzzz+vv/3tb8419HXlHTt2TF26dNHMmTNLPF+RPk1OTtaXX36pVatWadmyZVq3bp1GjRpVU2/Bb5TV18ePH9fWrVs1adIkbd26Ve+995527dqlG264wes6+vocGD/Vs2dPM2bMGOfrwsJCExMTY1JSUixWVbccOHDASDJr1641xhhz+PBhExwcbN5++23nmq+++spIMmlpabbK9FtHjhwxbdu2NatWrTJ9+vQxDz74oDGGfvalRx991PTu3bvU80VFRSY6Otq88MILzrHDhw8bt9tt3nzzzZoosc647rrrzF133eV1bMiQISY5OdkYQ1/7giTz/vvvO19XpE937txpJJnNmzc713z00UfG5XKZH3/8scZq9zdn9nVJNm3aZCSZvXv3GmPo63PllyOkJ0+eVHp6uhITE51jAQEBSkxMVFpamsXK6pbc3FxJUtOmTSVJ6enpKigo8Or39u3bq1WrVvR7FYwZM0bXXXedV39K9LMvffDBB+revbuGDRum5s2bq1u3bnr11Ved8xkZGcrOzvbq64iICMXHx9PXlXT55ZcrNTVVu3fvliR9/vnnWr9+va655hpJ9HV1qEifpqWlqUmTJurevbtzTWJiogICArRx48Yar7kuyc3NlcvlUpMmTSTR1+cqyHYBVfHzzz+rsLBQUVFRXsejoqL09ddfW6qqbikqKtK4cePUq1cvde7cWZKUnZ2tkJAQ53++YlFRUcrOzrZQpf9auHChtm7dqs2bN591jn72ne+++06zZs3S+PHj9dhjj2nz5s164IEHFBISohEjRjj9WdLPEvq6ciZMmCCPx6P27dsrMDBQhYWFevrpp5WcnCxJ9HU1qEifZmdnq3nz5l7ng4KC1LRpU/r9HOTl5enRRx/VLbfcovDwcEn09bnyy0CK6jdmzBjt2LFD69evt11KnZOZmakHH3xQq1atUmhoqO1y6rSioiJ1795dzzzzjCSpW7du2rFjh2bPnq0RI0ZYrq5uWbRokebPn68FCxaoU6dO2r59u8aNG6eYmBj6GnVKQUGBbrrpJhljNGvWLNvl1Bl++ZF9s2bNFBgYeNaq45ycHEVHR1uqqu4YO3asli1bptWrV+vCCy90jkdHR+vkyZM6fPiw1/X0e+Wkp6frwIEDuuSSSxQUFKSgoCCtXbtWM2bMUFBQkKKiouhnH2nRooU6duzodaxDhw7at2+fJDn9yc+Sc/fnP/9ZEyZM0PDhwxUXF6fbb79dDz30kFJSUiTR19WhIn0aHR191mLfU6dO6eDBg/R7FRSH0b1792rVqlXO6KhEX58rvwykISEhuvTSS5WamuocKyoqUmpqqhISEixW5t+MMRo7dqzef/99ffLJJ4qNjfU6f+mllyo4ONir33ft2qV9+/bR75UwYMAAffHFF9q+fbvz6N69u5KTk50/08++0atXr7O2Ltu9e7dat24tSYqNjVV0dLRXX3s8Hm3cuJG+rqTjx48rIMD7V0pgYKCKiook0dfVoSJ9mpCQoMOHDys9Pd255pNPPlFRUZHi4+NrvGZ/VhxG9+zZo48//liRkZFe5+nrc2R7VVVVLVy40LjdbjN37lyzc+dOM2rUKNOkSROTnZ1tuzS/NXr0aBMREWHWrFlj9u/f7zyOHz/uXHPvvfeaVq1amU8++cRs2bLFJCQkmISEBItV1w2nr7I3hn72lU2bNpmgoCDz9NNPmz179pj58+ebBg0amH/961/ONc8++6xp0qSJWbJkifnPf/5jBg0aZGJjY82JEycsVu5/RowYYS644AKzbNkyk5GRYd577z3TrFkz88gjjzjX0NeVd+TIEbNt2zazbds2I8lMmzbNbNu2zVnZXZE+vfrqq023bt3Mxo0bzfr1603btm3NLbfcYust1Vpl9fXJkyfNDTfcYC688EKzfft2r9+R+fn5Thv0ddX5bSA1xpi//e1vplWrViYkJMT07NnTbNiwwXZJfk1SiY85c+Y415w4ccLcd9995rzzzjMNGjQwN954o9m/f7+9ouuIMwMp/ew7S5cuNZ07dzZut9u0b9/evPLKK17ni4qKzKRJk0xUVJRxu91mwIABZteuXZaq9V8ej8c8+OCDplWrViY0NNRcfPHF5vHHH/f6ZU1fV97q1atL/Lk8YsQIY0zF+vSXX34xt9xyi2nUqJEJDw83I0eONEeOHLHwbmq3svo6IyOj1N+Rq1evdtqgr6vOZcxpt9EAAAAAaphfziEFAABA3UEgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVf8PUBGuc1bBKysAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slice=50\n",
    "sliced_img = mask[:,:,slice]\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(8,8))\n",
    "ax.imshow(sliced_img)\n",
    "\n",
    "ax.set_title('Mask (Traverse)', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7554a875",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = np.where(mask.reshape(-1)>0)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f34166c4",
   "metadata": {},
   "source": [
    "### Load data and build (feature, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d78b2d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████| 14471/14471 [14:57<00:00, 16.12it/s]\n"
     ]
    }
   ],
   "source": [
    "path = '/cis/home/jdey4/data_MRI/'\n",
    "subjects = os.listdir(path)\n",
    "X = []\n",
    "y = []\n",
    "file_no = 0\n",
    "IDs = set(df['ID'])\n",
    "quality_ID = set(df_quality['sub'])\n",
    "for subject in tqdm(subjects):\n",
    "    if subject in IDs and subject in quality_ID:\n",
    "        #print(df[df['ID']==subject]['Sex'])\n",
    "        IQR = list(df_quality[df_quality['sub']==subject]['Weighted average (IQR)'])[0]\n",
    "        \n",
    "        if IQR is np.nan:\n",
    "            continue\n",
    "            \n",
    "        if IQR[-1] == '%':\n",
    "            continue\n",
    "        \n",
    "        if float(IQR) < 60:\n",
    "            continue\n",
    "            \n",
    "        gender = list(df[df['ID']==subject]['Sex'])\n",
    "        sex = int(gender[0]=='FEMALE')\n",
    "        \n",
    "        \n",
    "        current_file = os.path.join(path, subject)\n",
    "        tmp = []\n",
    "        save = True\n",
    "        for file in glob.glob(current_file+'/mri/*'):\n",
    "            try:\n",
    "                img = nb.load(file).get_fdata()\n",
    "                file_no += 1\n",
    "                #print(img.shape)\n",
    "                tmp.append(img.reshape(-1)[idx])\n",
    "            except:\n",
    "                save = False\n",
    "                print(file,' corrupted')\n",
    "\n",
    "        '''if len(tmp)<2:\n",
    "            print(subject, ' has less files')'''\n",
    "            \n",
    "        if save and len(tmp)==2:\n",
    "            X.append(np.array(tmp).reshape(1,-1))\n",
    "            y.append(sex)\n",
    "    \n",
    "X = np.concatenate(X,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ec4c1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2206, 1298282)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "850679f0-402b-436b-8628-249276cc6fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5485040797824116"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e2e5d6d7-c496-48b1-96e1-c1dfbdf81df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13792"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_no"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11d7bc4",
   "metadata": {},
   "source": [
    "### Find the feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a2537ed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 100/100 [45:21<00:00, 27.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  0.8288009049773757\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "reps = 100\n",
    "sample_size_ratio = .8\n",
    "feature_map_diff = np.zeros(X.shape[1], dtype=float)\n",
    "accuracy = 0\n",
    "\n",
    "for ii in tqdm(range(reps)):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(\n",
    "                    X, y, train_size=sample_size_ratio,random_state=ii, stratify=y)\n",
    "    total_sample = len(x_train)\n",
    "    y_random = y_train.copy()\n",
    "    random.shuffle(y_random)\n",
    "    \n",
    "    clf_random = RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
    "    clf = RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
    "    \n",
    "    clf_random.fit(x_train,y_random)\n",
    "    clf.fit(x_train,y_train)\n",
    "\n",
    "    predicted_label = clf.predict(x_test)\n",
    "    accuracy += np.mean(predicted_label==y_test)\n",
    "    \n",
    "    random_importance_map = clf_random.feature_importances_\n",
    "    importance_map = clf.feature_importances_\n",
    "    diff = importance_map - random_importance_map\n",
    "    feature_idx = np.where(diff > 0)[0]\n",
    "    map_diff = np.zeros(X.shape[1], dtype=float)\n",
    "    map_diff[feature_idx] = 1\n",
    "    \n",
    "    feature_map_diff += map_diff\n",
    "\n",
    "feature_map_diff /= reps\n",
    "accuracy /= reps\n",
    "\n",
    "print('Accuracy is ',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "93b6932d",
   "metadata": {},
   "outputs": [],
   "source": [
    "demo_filepath_whitematter = '/cis/home/jdey4/data_MRI/sub-A00008326/mri/mwp2sub-A00008326_T1w.nii'\n",
    "demo_filepath_graymatter = '/cis/home/jdey4/data_MRI/sub-A00008326/mri/mwp1sub-A00008326_T1w.nii'\n",
    "\n",
    "img_whitematter = nb.load(demo_filepath_whitematter).get_fdata()\n",
    "img_graymatter = nb.load(demo_filepath_graymatter).get_fdata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f0d57c68-ab1a-4f68-98af-e76983875007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.07767978e-05, 7.11412948e-05, 9.61403667e-05, ...,\n",
       "       2.27352584e-05, 2.02377941e-05, 6.23278013e-05])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance_map[np.where(importance_map>0)[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2328ce98",
   "metadata": {},
   "outputs": [],
   "source": [
    "brain_img = 0*np.ones((113*137*113), dtype=float)\n",
    "brain_img[idx] = feature_map_diff[:len(idx)]\n",
    "brain_img = brain_img.reshape(113,137,113)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "eebba4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'White matter (Traverse)')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slice = 35\n",
    "\n",
    "sliced_img_whitematter = img_whitematter[:,:,slice]\n",
    "sliced_img_graymatter = img_graymatter[:,:,slice]\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(16,8))\n",
    "ax[0].imshow(brain_img[:,:,slice])\n",
    "ax[1].imshow(sliced_img_graymatter)\n",
    "\n",
    "ax[0].set_title('RF feature importance)', fontsize=20)\n",
    "ax[1].set_title('White matter (Traverse)', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81774ae3",
   "metadata": {},
   "source": [
    "### Todos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "514aaefc-931e-41b9-b1ef-66669e31cbed",
   "metadata": {},
   "source": [
    "1. do rank test\n",
    "2. report accuracy\n",
    "3. do morf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1f400e09-084c-4c00-adf1-2678080fed40",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_white = nb.load(demo_filepath_whitematter)\n",
    "img = nb.Nifti1Image(brain_img, header = img_white.header, affine = img_white.affine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "63f375e4-4ad1-4f4c-be1b-53c20474a555",
   "metadata": {},
   "outputs": [],
   "source": [
    "nb.save(img, 'feature_imp.nii') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f078b13e-9659-4803-aa3c-ed1b2c757461",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}