April3_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\n",
    "import pickle\n",
    "import scipy.stats as ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4edf30a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
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       "<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": "f34166c4",
   "metadata": {},
   "source": [
    "### Load data and build (feature, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d78b2d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|██████                                                                 | 1236/14471 [07:38<1:21:52,  2.69it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 27\u001b[0m\n\u001b[1;32m     25\u001b[0m count_ \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;66;03m#print(count, count_)\u001b[39;00m\n\u001b[0;32m---> 27\u001b[0m gender \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mID\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43msubject\u001b[49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSex\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m     28\u001b[0m sex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(gender[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFEMALE\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     31\u001b[0m current_file \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(path, subject)\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/frame.py:4081\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4079\u001b[0m \u001b[38;5;66;03m# Do we have a (boolean) 1d indexer?\u001b[39;00m\n\u001b[1;32m   4080\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[0;32m-> 4081\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_bool_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4083\u001b[0m \u001b[38;5;66;03m# We are left with two options: a single key, and a collection of keys,\u001b[39;00m\n\u001b[1;32m   4084\u001b[0m \u001b[38;5;66;03m# We interpret tuples as collections only for non-MultiIndex\u001b[39;00m\n\u001b[1;32m   4085\u001b[0m is_single_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_list_like(key)\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/frame.py:4143\u001b[0m, in \u001b[0;36mDataFrame._getitem_bool_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4140\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m   4142\u001b[0m indexer \u001b[38;5;241m=\u001b[39m key\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m-> 4143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_with_is_copy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/generic.py:4150\u001b[0m, in \u001b[0;36mNDFrame._take_with_is_copy\u001b[0;34m(self, indices, axis)\u001b[0m\n\u001b[1;32m   4139\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m   4140\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_with_is_copy\u001b[39m(\u001b[38;5;28mself\u001b[39m, indices, axis: Axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[1;32m   4141\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   4142\u001b[0m \u001b[38;5;124;03m    Internal version of the `take` method that sets the `_is_copy`\u001b[39;00m\n\u001b[1;32m   4143\u001b[0m \u001b[38;5;124;03m    attribute to keep track of the parent dataframe (using in indexing\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4148\u001b[0m \u001b[38;5;124;03m    See the docstring of `take` for full explanation of the parameters.\u001b[39;00m\n\u001b[1;32m   4149\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4150\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4151\u001b[0m     \u001b[38;5;66;03m# Maybe set copy if we didn't actually change the index.\u001b[39;00m\n\u001b[1;32m   4152\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m result\u001b[38;5;241m.\u001b[39m_get_axis(axis)\u001b[38;5;241m.\u001b[39mequals(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis(axis)):\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/generic.py:4130\u001b[0m, in \u001b[0;36mNDFrame.take\u001b[0;34m(self, indices, axis, **kwargs)\u001b[0m\n\u001b[1;32m   4125\u001b[0m     \u001b[38;5;66;03m# We can get here with a slice via DataFrame.__getitem__\u001b[39;00m\n\u001b[1;32m   4126\u001b[0m     indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\n\u001b[1;32m   4127\u001b[0m         indices\u001b[38;5;241m.\u001b[39mstart, indices\u001b[38;5;241m.\u001b[39mstop, indices\u001b[38;5;241m.\u001b[39mstep, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mintp\n\u001b[1;32m   4128\u001b[0m     )\n\u001b[0;32m-> 4130\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4131\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4132\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_block_manager_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4133\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   4134\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\u001b[38;5;241m.\u001b[39m__finalize__(\n\u001b[1;32m   4136\u001b[0m     \u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   4137\u001b[0m )\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/internals/managers.py:894\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[0;34m(self, indexer, axis, verify)\u001b[0m\n\u001b[1;32m    891\u001b[0m indexer \u001b[38;5;241m=\u001b[39m maybe_convert_indices(indexer, n, verify\u001b[38;5;241m=\u001b[39mverify)\n\u001b[1;32m    893\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis]\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m--> 894\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex_indexer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    895\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnew_axis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    896\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    897\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    898\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_dups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    899\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    900\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/internals/managers.py:687\u001b[0m, in \u001b[0;36mBaseBlockManager.reindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)\u001b[0m\n\u001b[1;32m    680\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slice_take_blocks_ax0(\n\u001b[1;32m    681\u001b[0m         indexer,\n\u001b[1;32m    682\u001b[0m         fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[1;32m    683\u001b[0m         only_slice\u001b[38;5;241m=\u001b[39monly_slice,\n\u001b[1;32m    684\u001b[0m         use_na_proxy\u001b[38;5;241m=\u001b[39muse_na_proxy,\n\u001b[1;32m    685\u001b[0m     )\n\u001b[1;32m    686\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 687\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    688\u001b[0m         blk\u001b[38;5;241m.\u001b[39mtake_nd(\n\u001b[1;32m    689\u001b[0m             indexer,\n\u001b[1;32m    690\u001b[0m             axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m    691\u001b[0m             fill_value\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    692\u001b[0m                 fill_value \u001b[38;5;28;01mif\u001b[39;00m fill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mfill_value\n\u001b[1;32m    693\u001b[0m             ),\n\u001b[1;32m    694\u001b[0m         )\n\u001b[1;32m    695\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks\n\u001b[1;32m    696\u001b[0m     ]\n\u001b[1;32m    698\u001b[0m new_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m    699\u001b[0m new_axes[axis] \u001b[38;5;241m=\u001b[39m new_axis\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/internals/managers.py:688\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    680\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slice_take_blocks_ax0(\n\u001b[1;32m    681\u001b[0m         indexer,\n\u001b[1;32m    682\u001b[0m         fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[1;32m    683\u001b[0m         only_slice\u001b[38;5;241m=\u001b[39monly_slice,\n\u001b[1;32m    684\u001b[0m         use_na_proxy\u001b[38;5;241m=\u001b[39muse_na_proxy,\n\u001b[1;32m    685\u001b[0m     )\n\u001b[1;32m    686\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    687\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m--> 688\u001b[0m         \u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake_nd\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    689\u001b[0m \u001b[43m            \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    690\u001b[0m \u001b[43m            \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    691\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    692\u001b[0m \u001b[43m                \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfill_value\u001b[49m\n\u001b[1;32m    693\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    694\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    695\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks\n\u001b[1;32m    696\u001b[0m     ]\n\u001b[1;32m    698\u001b[0m new_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m    699\u001b[0m new_axes[axis] \u001b[38;5;241m=\u001b[39m new_axis\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/internals/blocks.py:1307\u001b[0m, in \u001b[0;36mBlock.take_nd\u001b[0;34m(self, indexer, axis, new_mgr_locs, fill_value)\u001b[0m\n\u001b[1;32m   1304\u001b[0m     allow_fill \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m   1306\u001b[0m \u001b[38;5;66;03m# Note: algos.take_nd has upcast logic similar to coerce_to_target_dtype\u001b[39;00m\n\u001b[0;32m-> 1307\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43malgos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake_nd\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1308\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_fill\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_fill\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\n\u001b[1;32m   1309\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1311\u001b[0m \u001b[38;5;66;03m# Called from three places in managers, all of which satisfy\u001b[39;00m\n\u001b[1;32m   1312\u001b[0m \u001b[38;5;66;03m#  these assertions\u001b[39;00m\n\u001b[1;32m   1313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ExtensionBlock):\n\u001b[1;32m   1314\u001b[0m     \u001b[38;5;66;03m# NB: in this case, the 'axis' kwarg will be ignored in the\u001b[39;00m\n\u001b[1;32m   1315\u001b[0m     \u001b[38;5;66;03m#  algos.take_nd call above.\u001b[39;00m\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/array_algos/take.py:117\u001b[0m, in \u001b[0;36mtake_nd\u001b[0;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mtake(indexer, fill_value\u001b[38;5;241m=\u001b[39mfill_value, allow_fill\u001b[38;5;241m=\u001b[39mallow_fill)\n\u001b[1;32m    116\u001b[0m arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(arr)\n\u001b[0;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_take_nd_ndarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_fill\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/array_algos/take.py:133\u001b[0m, in \u001b[0;36m_take_nd_ndarray\u001b[0;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    131\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m ensure_platform_int(indexer)\n\u001b[0;32m--> 133\u001b[0m dtype, fill_value, mask_info \u001b[38;5;241m=\u001b[39m \u001b[43m_take_preprocess_indexer_and_fill_value\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[43m    \u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_fill\u001b[49m\n\u001b[1;32m    135\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    137\u001b[0m flip_order \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mf_contiguous:\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/array_algos/take.py:579\u001b[0m, in \u001b[0;36m_take_preprocess_indexer_and_fill_value\u001b[0;34m(arr, indexer, fill_value, allow_fill, mask)\u001b[0m\n\u001b[1;32m    575\u001b[0m     mask_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    576\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    577\u001b[0m     \u001b[38;5;66;03m# check for promotion based on types only (do this first because\u001b[39;00m\n\u001b[1;32m    578\u001b[0m     \u001b[38;5;66;03m# it's faster than computing a mask)\u001b[39;00m\n\u001b[0;32m--> 579\u001b[0m     dtype, fill_value \u001b[38;5;241m=\u001b[39m \u001b[43mmaybe_promote\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    580\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;241m!=\u001b[39m arr\u001b[38;5;241m.\u001b[39mdtype:\n\u001b[1;32m    581\u001b[0m         \u001b[38;5;66;03m# check if promotion is actually required based on indexer\u001b[39;00m\n\u001b[1;32m    582\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:591\u001b[0m, in \u001b[0;36mmaybe_promote\u001b[0;34m(dtype, fill_value)\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[38;5;66;03m# for performance, we are using a cached version of the actual implementation\u001b[39;00m\n\u001b[1;32m    586\u001b[0m \u001b[38;5;66;03m# of the function in _maybe_promote. However, this doesn't always work (in case\u001b[39;00m\n\u001b[1;32m    587\u001b[0m \u001b[38;5;66;03m# of non-hashable arguments), so we fallback to the actual implementation if needed\u001b[39;00m\n\u001b[1;32m    588\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    589\u001b[0m     \u001b[38;5;66;03m# error: Argument 3 to \"__call__\" of \"_lru_cache_wrapper\" has incompatible type\u001b[39;00m\n\u001b[1;32m    590\u001b[0m     \u001b[38;5;66;03m# \"Type[Any]\"; expected \"Hashable\"  [arg-type]\u001b[39;00m\n\u001b[0;32m--> 591\u001b[0m     dtype, fill_value \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_promote_cached\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    592\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m)\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m    593\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    594\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m    595\u001b[0m     \u001b[38;5;66;03m# if fill_value is not hashable (required for caching)\u001b[39;00m\n\u001b[1;32m    596\u001b[0m     dtype, fill_value \u001b[38;5;241m=\u001b[39m _maybe_promote(dtype, fill_value)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "path = '/cis/home/jdey4/data_MRI/'\n",
    "subjects = os.listdir(path)\n",
    "X = []\n",
    "y = []\n",
    "file_no = 0\n",
    "count = 0\n",
    "count_ = 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",
    "        count += 1\n",
    "        #print(IQR)\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",
    "        count_ += 1\n",
    "        #print(count, count_)\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",
    "        file_count = 0\n",
    "        files = glob.glob(current_file+'/mri/*')\n",
    "\n",
    "        for file in files:\n",
    "            if 'mwp1' in file:\n",
    "                try:\n",
    "                    img = nb.load(file).get_fdata()\n",
    "                    file_count +=1 \n",
    "                except:\n",
    "                    break\n",
    "                \n",
    "        for file in files:\n",
    "            if 'mwp2' in file:\n",
    "                try:\n",
    "                    img = np.concatenate((img, nb.load(file).get_fdata()),axis=0)\n",
    "                    file_count +=1 \n",
    "                except:\n",
    "                    break\n",
    "                \n",
    "\n",
    "        '''if len(tmp)<2:\n",
    "            print(subject, ' has less files')'''\n",
    "           \n",
    "        if file_count==2:\n",
    "            X.append(img.reshape(1,-1))\n",
    "            y.append(sex)\n",
    "\n",
    "    \n",
    "X = np.concatenate(X,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a98ee052-5b6d-4268-aaf8-7ce7ad95ac34",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ec4c1b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "850679f0-402b-436b-8628-249276cc6fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3beb82d9-43c0-4325-8994-8bd9e653206d",
   "metadata": {},
   "outputs": [],
   "source": [
    "slice = 39\n",
    "fig, ax = plt.subplots(1,1, figsize=(16,8))\n",
    "ax.imshow(X[0].reshape(113*2,137,113)[:,:,slice])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3a948de-134c-450d-a4b5-6ed98324092a",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_models = 1\n",
    "idx = list(range(len(y)))\n",
    "\n",
    "np.random.seed(0)\n",
    "np.random.shuffle(idx)\n",
    "train_samples = int(len(y)*0.8)\n",
    "test_samples = len(y) - train_samples\n",
    "train_ids = idx[:train_samples]\n",
    "\n",
    "for ii in tqdm(range(total_models)):\n",
    "    np.random.seed(ii)\n",
    "    np.random.shuffle(train_ids)\n",
    "    idx_chosen = train_ids[:int(len(y)*0.8*0.7)]\n",
    "    \n",
    "    rf = RandomForestClassifier(n_estimators=1, n_jobs=-1)\n",
    "    rf.fit(X[idx_chosen], y[idx_chosen])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c9d4cd8-bd09-4f3f-b14a-d457600e38eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('morf_models/model0.pickle','rb') as f:\n",
    "    rf = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ce15f4d-405c-4982-aad6-84b30a7dfbbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = rf.estimators_[0].tree_.get_projection_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34fed42e-1466-48d7-9c32-fe3b3b754f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(433, 3498706)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813e2522-6f35-4475-a0ff-a7bf71b2761a",
   "metadata": {},
   "source": [
    "## Train MORF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c767dc2a-0ad0-46a3-aad5-d6f4ce2940e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|███████████████████████████████▊                | 664/1000 [12:59:53<6:34:38, 70.47s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[18], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m x_train_, x_test, y_train_, y_test \u001b[38;5;241m=\u001b[39m train_test_split(\n\u001b[1;32m      3\u001b[0m                         X, y, train_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.8\u001b[39m,random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, stratify\u001b[38;5;241m=\u001b[39my)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ii \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(total_models)):\n\u001b[0;32m----> 5\u001b[0m     x_train, _, y_train, _ \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_test_split\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mx_train_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m.7\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mii\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstratify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my_train_\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m     morf \u001b[38;5;241m=\u001b[39m PatchObliqueRandomForestClassifier(n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, max_patch_dims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray((\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m4\u001b[39m)), data_dims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray((\u001b[38;5;241m113\u001b[39m, \u001b[38;5;241m137\u001b[39m, \u001b[38;5;241m113\u001b[39m)), n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      9\u001b[0m     morf\u001b[38;5;241m.\u001b[39mfit(x_train[:,:\u001b[38;5;241m113\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m137\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m113\u001b[39m], y_train)\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/sklearn/utils/_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    211\u001b[0m         )\n\u001b[1;32m    212\u001b[0m     ):\n\u001b[0;32m--> 213\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    215\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m    217\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m    219\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[1;32m    220\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    221\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    222\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m    223\u001b[0m     )\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/sklearn/model_selection/_split.py:2683\u001b[0m, in \u001b[0;36mtrain_test_split\u001b[0;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[1;32m   2679\u001b[0m     cv \u001b[38;5;241m=\u001b[39m CVClass(test_size\u001b[38;5;241m=\u001b[39mn_test, train_size\u001b[38;5;241m=\u001b[39mn_train, random_state\u001b[38;5;241m=\u001b[39mrandom_state)\n\u001b[1;32m   2681\u001b[0m     train, test \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(cv\u001b[38;5;241m.\u001b[39msplit(X\u001b[38;5;241m=\u001b[39marrays[\u001b[38;5;241m0\u001b[39m], y\u001b[38;5;241m=\u001b[39mstratify))\n\u001b[0;32m-> 2683\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2684\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_iterable\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2685\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[43m_safe_indexing\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_safe_indexing\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43marrays\u001b[49m\n\u001b[1;32m   2686\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2687\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/sklearn/model_selection/_split.py:2685\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   2679\u001b[0m     cv \u001b[38;5;241m=\u001b[39m CVClass(test_size\u001b[38;5;241m=\u001b[39mn_test, train_size\u001b[38;5;241m=\u001b[39mn_train, random_state\u001b[38;5;241m=\u001b[39mrandom_state)\n\u001b[1;32m   2681\u001b[0m     train, test \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(cv\u001b[38;5;241m.\u001b[39msplit(X\u001b[38;5;241m=\u001b[39marrays[\u001b[38;5;241m0\u001b[39m], y\u001b[38;5;241m=\u001b[39mstratify))\n\u001b[1;32m   2683\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\n\u001b[1;32m   2684\u001b[0m     chain\u001b[38;5;241m.\u001b[39mfrom_iterable(\n\u001b[0;32m-> 2685\u001b[0m         (_safe_indexing(a, train), \u001b[43m_safe_indexing\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m arrays\n\u001b[1;32m   2686\u001b[0m     )\n\u001b[1;32m   2687\u001b[0m )\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/sklearn/utils/__init__.py:411\u001b[0m, in \u001b[0;36m_safe_indexing\u001b[0;34m(X, indices, axis)\u001b[0m\n\u001b[1;32m    409\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _polars_indexing(X, indices, indices_dtype, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m    410\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 411\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_array_indexing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindices_dtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    412\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    413\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _list_indexing(X, indices, indices_dtype)\n",
      "File \u001b[0;32m~/my_documents/cmi/lib/python3.9/site-packages/sklearn/utils/__init__.py:208\u001b[0m, in \u001b[0;36m_array_indexing\u001b[0;34m(array, key, key_dtype, axis)\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    207\u001b[0m     key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m--> 208\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marray\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m array[:, key]\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "total_models = 200\n",
    "idx = list(np.range(len(y)))\n",
    "\n",
    "np.random.seed(0)\n",
    "np.random.shuffle(idx)\n",
    "train_samples = int(len(y)*0.8)\n",
    "test_samples = len(y) - train_samples\n",
    "train_ids = idx[:train_samples]\n",
    "\n",
    "for ii in tqdm(range(total_models)):\n",
    "    np.random.seed(ii)\n",
    "    np.random.shuffle(train_ids)\n",
    "    idx_chosen = train_ids[:int(len(y)*0.8*0.7)]\n",
    "    \n",
    "    morf = PatchObliqueRandomForestClassifier(n_estimators=1, max_patch_dims=np.array((3, 3, 3)), data_dims=np.array((113*2, 137, 113)), n_jobs=-1)\n",
    "    morf.fit(X[idx_chosen], y_train[idx_chosen])\n",
    "\n",
    "    with open('morf_models/model'+str(ii)+'.pickle','wb') as f:\n",
    "        pickle.dump(morf, f)\n",
    "\n",
    "    del morf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "65c32e62-2dbe-46d7-bed8-450d155f7ea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MORF accuracy 0.8642533936651584\n"
     ]
    }
   ],
   "source": [
    "total_models = 200\n",
    "idx = list(np.range(len(y)))\n",
    "\n",
    "np.random.seed(0)\n",
    "np.random.shuffle(idx)\n",
    "train_samples = int(len(y)*0.8)\n",
    "test_samples = len(y) - train_samples\n",
    "train_ids = idx[:train_samples]\n",
    "\n",
    "predicted_proba_ = []\n",
    "total_models = 650\n",
    "for ii in tqdm(range(total_models)):\n",
    "    with open('morf_models/model'+str(ii)+'.pickle','rb') as f:\n",
    "        morf = pickle.load(f)\n",
    "        predicted_proba_.append(\n",
    "            morf.predict_proba(x_test[:,:113*137*113])\n",
    "        )\n",
    "        del morf\n",
    "\n",
    "predicted_proba = np.mean(predicted_proba_,axis=0)\n",
    "predicted_label = np.argmax(predicted_proba,axis=1)\n",
    "print('MORF accuracy', np.mean(predicted_label==y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa3282b8-45ed-4a4f-8c46-fad847d68400",
   "metadata": {},
   "source": [
    "## Train MORF with shuffled label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6e75748-bae6-40ac-a441-b59b11d17fe6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████| 650/650 [13:46:30<00:00, 76.29s/it]\n"
     ]
    }
   ],
   "source": [
    "total_models = 650\n",
    "x_train_, x_test, y_train_, y_test = train_test_split(\n",
    "                        X, y, train_size=.8,random_state=0, stratify=y)\n",
    "for ii in tqdm(range(total_models)):\n",
    "    x_train, _, y_train, _ = train_test_split(\n",
    "                        x_train_, y_train_, train_size=.7,random_state=ii, stratify=y_train_)\n",
    "\n",
    "    random.shuffle(y_train)\n",
    "    morf = PatchObliqueRandomForestClassifier(n_estimators=1, max_patch_dims=np.array((4, 4, 4)), data_dims=np.array((2*113, 137, 113)), n_jobs=-1)\n",
    "    morf.fit(x_train, y_train)\n",
    "\n",
    "    with open('morf_shuffled_models/model'+str(ii)+'.pickle','wb') as f:\n",
    "        pickle.dump(morf, f)\n",
    "\n",
    "    del morf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11d7bc4",
   "metadata": {},
   "source": [
    "### Find the feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2537ed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 200/200 [4:47:57<00:00, 86.39s/it]\n",
      "100%|█████████████████████████████████████████████████████████████████| 200/200 [6:57:08<00:00, 125.14s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"with open('feature_imp.pickle','wb') as f:\\n    pickle.dump(feature_imp, f)\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_models = 650\n",
    "feature_imp = []\n",
    "for ii in tqdm(range(total_models)):\n",
    "    with open('morf_models/model'+str(ii)+'.pickle','rb') as f:\n",
    "        morf = pickle.load(f)\n",
    "        feature_imp.append(\n",
    "            morf.feature_importances_\n",
    "        )\n",
    "        del morf\n",
    "\n",
    "for ii in tqdm(range(total_models)):\n",
    "    with open('morf_shuffled_models/model'+str(ii)+'.pickle','rb') as f:\n",
    "        morf = pickle.load(f)\n",
    "        feature_imp.append(\n",
    "            morf.feature_importances_\n",
    "        )\n",
    "        del morf\n",
    "\n",
    "'''with open('feature_imp.pickle','wb') as f:\n",
    "    pickle.dump(feature_imp, f)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5569080b-c714-4226-b0bc-fdcee3e718f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_imp = np.array(feature_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f1064d7-a2a6-4eff-ac95-a7366bae437b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('feature_imp.pickle','wb') as f:\n",
    "    pickle.dump(feature_imp, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d875254c-cc71-4d08-a80a-56f75f24958a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 100/100 [14:27<00:00,  8.67s/it]\n"
     ]
    }
   ],
   "source": [
    "reps = 100\n",
    "\n",
    "r = ss.rankdata(\n",
    "            np.mean(feature_imp[:total_models],axis=0), method='max'\n",
    "        )\n",
    "r_0 = ss.rankdata(\n",
    "            np.mean(feature_imp[total_models:],axis=0), method='max'\n",
    "        )\n",
    "\n",
    "stat = (r_0 - r)\n",
    "\n",
    "count = np.zeros(len(feature_imp[0]))\n",
    "for _ in tqdm(range(reps)):\n",
    "    idx = list(range(2*total_models))\n",
    "    random.shuffle(idx)\n",
    "    \n",
    "    r1 = ss.rankdata(\n",
    "                np.mean(feature_imp[idx[:total_models]],axis=0), method='max'\n",
    "            )\n",
    "    r1_0 = ss.rankdata(\n",
    "                np.mean(feature_imp[idx[total_models:]],axis=0), method='max'\n",
    "            )\n",
    "    \n",
    "    stat1 = (r1_0 - r1)\n",
    "        \n",
    "    count += (stat1>=stat)*1\n",
    "\n",
    "p_val = (1 + count)/(1+reps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "22cb10a5-301e-47c9-abb0-e979e02cc24e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-123694, -123694, -123694, ..., -123694, -123694, -123694])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c46a0873-2f9a-4cd5-a118-e153e4bd0e66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-484884, -484881, -484820, ...,  489554,  489621,  489734])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(stat1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4421c9d-7499-472b-8c3c-f70aa2e46773",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('feature_imp.pickle','rb') as f:\n",
    "    feature_imp = pickle.load(f)\n",
    "\n",
    "total_models = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18226443-3eb2-4c50-8001-3888ea02feb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 69%|████████████████████████████████████████████████▏                     | 689/1000 [14:37:56<6:08:32, 71.10s/it]"
     ]
    }
   ],
   "source": [
    "reps = 1000\n",
    "\n",
    "stat = np.zeros(len(feature_imp[0]))\n",
    "for ii in range(total_models):\n",
    "    r = ss.rankdata(\n",
    "            1-feature_imp[ii], method='max'\n",
    "        )\n",
    "    r_0 = ss.rankdata(\n",
    "            1-feature_imp[total_models+ii], method='max'\n",
    "        )\n",
    "\n",
    "    stat += (r_0 > r)*1\n",
    "\n",
    "stat /= total_models\n",
    "\n",
    "count = np.zeros(len(feature_imp[0]))\n",
    "for _ in tqdm(range(reps)):\n",
    "    idx = list(range(2*total_models))\n",
    "    random.shuffle(idx)\n",
    "    stat1 = np.zeros(len(feature_imp[0]))\n",
    "    \n",
    "    for ii in range(total_models):\n",
    "        r1 = ss.rankdata(\n",
    "                1-feature_imp[idx[ii]], method='max'\n",
    "            )\n",
    "        r1_0 = ss.rankdata(\n",
    "                1-feature_imp[idx[total_models+ii]], method='max'\n",
    "            )\n",
    "    \n",
    "        stat1 += (r1_0 > r1)*1\n",
    "\n",
    "    stat1 /= total_models\n",
    "    count += (stat1>=stat)*1\n",
    "\n",
    "p_val = (1 + count)/(1+reps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c226433b-c63f-424d-9cad-d43e9cdf600e",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''id_accept = np.where(p_val>0.05)[0]\n",
    "id_reject = np.where(p_val<=0.05)[0]\n",
    "\n",
    "feature_map = np.zeros(len(feature_imp[0]))\n",
    "feature_map[id_reject] = 1'''\n",
    "feature_map = -np.log(p_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5007a7e-5256-48e7-9049-71c444cef9a0",
   "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": 12,
   "id": "af2eb6ee-ed2b-4fd1-9ad0-8cc50aabe18c",
   "metadata": {},
   "outputs": [],
   "source": [
    "brain_img = feature_map.reshape(113*2,137,113)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0736d6a5-f072-4260-af9a-e07c0f319443",
   "metadata": {},
   "outputs": [],
   "source": [
    "brain_img_ = np.mean(feature_imp[:total_models],axis=0).reshape(113*2,137,113)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f2171f45-62d8-45b6-afcf-00a003fbef2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'gray matter (Traverse)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slice = 39\n",
    "\n",
    "sliced_img_whitematter = img_whitematter[:,:,slice]\n",
    "sliced_img_graymatter = img_graymatter[:,:,slice]\n",
    "\n",
    "fig, ax = plt.subplots(1,3, figsize=(24,8))\n",
    "ax[0].imshow(brain_img[:,:,slice])\n",
    "ax[1].imshow(brain_img_[:,:,slice])\n",
    "ax[2].imshow(sliced_img_graymatter)\n",
    "\n",
    "ax[0].set_title('MORF feature importance -log(p)', fontsize=20)\n",
    "ax[1].set_title('MORF feature importance', fontsize=20)\n",
    "ax[2].set_title('gray matter (Traverse)', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e2db63c6-e211-4c57-b493-6e6c73b034ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_gray = nb.load(demo_filepath_graymatter)\n",
    "img = nb.Nifti1Image(brain_img[:113,:,:], header = img_gray.header, affine = img_gray.affine)\n",
    "\n",
    "nb.save(img, 'feature_imp_gray.nii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0288f4a0-ece7-4440-98f2-a30108b71720",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_gray = nb.load(demo_filepath_graymatter)\n",
    "img = nb.Nifti1Image(brain_img[113:,:,:], header = img_gray.header, affine = img_gray.affine)\n",
    "\n",
    "nb.save(img, 'feature_imp_white.nii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6526af4e-25c0-46a1-93fc-249e2b793e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.00990099, 0.00990099,\n",
       "       0.00990099, 0.00990099, 0.00990099, 0.01980198, 0.01980198,\n",
       "       0.01980198, 0.01980198, 0.01980198, 0.01980198, 0.01980198,\n",
       "       0.01980198, 0.01980198, 0.01980198, 0.01980198, 0.01980198,\n",
       "       0.01980198, 0.01980198, 0.01980198, 0.01980198, 0.01980198,\n",
       "       0.01980198, 0.01980198, 0.01980198, 0.01980198, 0.01980198,\n",
       "       0.01980198, 0.01980198, 0.01980198, 0.01980198, 0.01980198,\n",
       "       0.01980198, 0.01980198, 0.01980198, 0.01980198, 0.01980198,\n",
       "       0.01980198, 0.01980198, 0.01980198, 0.01980198, 0.01980198])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(p_val)[:200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "88de7ac1-b96d-4de2-b7c5-0ddc60286470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  11351.,   16307.,   28649.,   13024.,   57801.,   63065.,\n",
       "          32777.,   74499.,   63955., 1387925.]),\n",
       " array([0.00990099, 0.10891089, 0.20792079, 0.30693069, 0.40594059,\n",
       "        0.5049505 , 0.6039604 , 0.7029703 , 0.8019802 , 0.9009901 ,\n",
       "        1.        ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nna6EzJs3TyEhIZo3b56OHDmiyy+/XFlZWXr66acv3KMAAAABK8QKgNdKPB6PnE6nmpubFRUVZXocAAC6lVhYaXoE2w4vmdInt9vT52/+Ng0AADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRvYqRsrIyJSYmKjIyUmlpadqxY8d51588eVL5+fmKj4+Xw+HQyJEjtWXLll4NDAAAgku43QM2bNiggoIClZeXKy0tTaWlpcrMzNT+/fsVExNzzvr29nbdcccdiomJ0aZNmzRkyBB9/vnnGjhw4IWYHwAABDjbMbJ8+XLNnDlTeXl5kqTy8nJVVlZqzZo1KiwsPGf9mjVrdOLECX388ce65JJLJEmJiYk/bGoAABA0bL1M097ertraWmVkZHx3A6GhysjIUE1NTZfHvPnmm0pPT1d+fr5iY2M1atQoLV68WB0dHd3eT1tbmzweT6cNAAAEJ1sxcvz4cXV0dCg2NrbT/tjYWLnd7i6Pqa+v16ZNm9TR0aEtW7Zo/vz5WrZsmZ566qlu76ekpEROp9O3uVwuO2MCAIAA0uefpvF6vYqJidHKlSuVmpqq7OxszZ07V+Xl5d0eU1RUpObmZt/W2NjY12MCAABDbL1nJDo6WmFhYWpqauq0v6mpSXFxcV0eEx8fr0suuURhYWG+fddee63cbrfa29sVERFxzjEOh0MOh8POaAAAIEDZujISERGh1NRUVVVV+fZ5vV5VVVUpPT29y2PGjx+vgwcPyuv1+vYdOHBA8fHxXYYIAAC4uNh+maagoECrVq3Syy+/rL179+rBBx9Ua2ur79M1OTk5Kioq8q1/8MEHdeLECc2ePVsHDhxQZWWlFi9erPz8/Av3KAAAQMCy/dHe7OxsHTt2TAsWLJDb7VZycrK2bt3qe1NrQ0ODQkO/axyXy6V33nlHc+bM0ZgxYzRkyBDNnj1bjz/++IV7FAAAIGCFWJZlmR7i+3g8HjmdTjU3NysqKsr0OAAAdCuxsNL0CLYdXjKlT263p8/f/G0aAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo3oVI2VlZUpMTFRkZKTS0tK0Y8eOHh1XUVGhkJAQTZ06tTd3CwAAgpDtGNmwYYMKCgpUXFysXbt2KSkpSZmZmfryyy/Pe9zhw4f16KOPasKECb0eFgAABB/bMbJ8+XLNnDlTeXl5uu6661ReXq5LL71Ua9as6faYjo4O3XfffVq4cKGGDx/+gwYGAADBxVaMtLe3q7a2VhkZGd/dQGioMjIyVFNT0+1xf/rTnxQTE6P777+/R/fT1tYmj8fTaQMAAMHJVowcP35cHR0dio2N7bQ/NjZWbre7y2M+/PBDrV69WqtWrerx/ZSUlMjpdPo2l8tlZ0wAABBA+vTTNC0tLZo+fbpWrVql6OjoHh9XVFSk5uZm39bY2NiHUwIAAJPC7SyOjo5WWFiYmpqaOu1vampSXFzcOes/++wzHT58WFlZWb59Xq/3mzsOD9f+/fs1YsSIc45zOBxyOBx2RgMAAAHK1pWRiIgIpaamqqqqyrfP6/WqqqpK6enp56y/5pprtGfPHtXV1fm2u+66SxMnTlRdXR0vvwAAAHtXRiSpoKBAubm5Gjt2rMaNG6fS0lK1trYqLy9PkpSTk6MhQ4aopKREkZGRGjVqVKfjBw4cKEnn7AcAABcn2zGSnZ2tY8eOacGCBXK73UpOTtbWrVt9b2ptaGhQaChf7AoAAHomxLIsy/QQ38fj8cjpdKq5uVlRUVGmxwEAoFuJhZWmR7Dt8JIpfXK7PX3+5hIGAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUb2KkbKyMiUmJioyMlJpaWnasWNHt2tXrVqlCRMmaNCgQRo0aJAyMjLOux4AAFxcbMfIhg0bVFBQoOLiYu3atUtJSUnKzMzUl19+2eX66upq3Xvvvfrggw9UU1Mjl8ulO++8U0eOHPnBwwMAgMAXYlmWZeeAtLQ03XjjjXr++eclSV6vVy6XS4888ogKCwu/9/iOjg4NGjRIzz//vHJycnp0nx6PR06nU83NzYqKirIzLgAAfpVYWGl6BNsOL5nSJ7fb0+dvW1dG2tvbVVtbq4yMjO9uIDRUGRkZqqmp6dFtnD59WmfOnNHgwYO7XdPW1iaPx9NpAwAAwclWjBw/flwdHR2KjY3ttD82NlZut7tHt/H4448rISGhU9D8r5KSEjmdTt/mcrnsjAkAAAKIXz9Ns2TJElVUVOiNN95QZGRkt+uKiorU3Nzs2xobG/04JQAA8KdwO4ujo6MVFhampqamTvubmpoUFxd33mOXLl2qJUuW6L333tOYMWPOu9bhcMjhcNgZDQAABChbV0YiIiKUmpqqqqoq3z6v16uqqiqlp6d3e9yzzz6rRYsWaevWrRo7dmzvpwUAAEHH1pURSSooKFBubq7Gjh2rcePGqbS0VK2trcrLy5Mk5eTkaMiQISopKZEkPfPMM1qwYIHWr1+vxMRE33tL+vfvr/79+1/AhwIAAAKR7RjJzs7WsWPHtGDBArndbiUnJ2vr1q2+N7U2NDQoNPS7Cy4vvPCC2tvbdffdd3e6neLiYj355JM/bHoAABDwbH/PiAl8zwgAIFDwPSPf6ZPvGQEAALjQiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYFW56AACAfyQWVpoewbbDS6aYHgF+wJURAABgFDECAACM4mUaAMbx8gG6E4i/G7CPKyMAAMAorowAQYb/Jwkg0HBlBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRfJoGAHqBTy0BFw4xAr8I1P9w88VWAND3eJkGAAAYRYwAAACjevUyTVlZmf785z/L7XYrKSlJzz33nMaNG9ft+o0bN2r+/Pk6fPiwrrrqKj3zzDOaPHlyr4e+2AXqSx4AAHTFdoxs2LBBBQUFKi8vV1pamkpLS5WZman9+/crJibmnPUff/yx7r33XpWUlOgXv/iF1q9fr6lTp2rXrl0aNWrUBXkQPwRP7Dgffj8AoO+FWJZl2TkgLS1NN954o55//nlJktfrlcvl0iOPPKLCwsJz1mdnZ6u1tVVvv/22b99NN92k5ORklZeX9+g+PR6PnE6nmpubFRUVZWfc78WTDQDgYtdXb9bv6fO3rSsj7e3tqq2tVVFRkW9faGioMjIyVFNT0+UxNTU1Kigo6LQvMzNTmzdv7vZ+2tra1NbW5vu5ublZ0jcP6kLztp2+4LcJAEAg6Yvn1/9/u9933cNWjBw/flwdHR2KjY3ttD82Nlb79u3r8hi3293lerfb3e39lJSUaOHChefsd7lcdsYFAAA94Czt29tvaWmR0+ns9t9/lN8zUlRU1Olqitfr1YkTJ3TZZZcpJCSk17fr8XjkcrnU2Nh4wV/uQWeca//hXPsP59p/ONf+05fn2rIstbS0KCEh4bzrbMVIdHS0wsLC1NTU1Gl/U1OT4uLiujwmLi7O1npJcjgccjgcnfYNHDjQzqjnFRUVxS+3n3Cu/Ydz7T+ca//hXPtPX53r810R+Zat7xmJiIhQamqqqqqqfPu8Xq+qqqqUnp7e5THp6emd1kvSu+++2+16AABwcbH9Mk1BQYFyc3M1duxYjRs3TqWlpWptbVVeXp4kKScnR0OGDFFJSYkkafbs2br11lu1bNkyTZkyRRUVFdq5c6dWrlx5YR8JAAAISLZjJDs7W8eOHdOCBQvkdruVnJysrVu3+t6k2tDQoNDQ7y643HzzzVq/fr3mzZunJ554QldddZU2b95s5DtGHA6HiouLz3kJCBce59p/ONf+w7n2H861//wYzrXt7xkBAAC4kPjbNAAAwChiBAAAGEWMAAAAo4gRAABgVNDFSFlZmRITExUZGam0tDTt2LHjvOs3btyoa665RpGRkRo9erS2bNnip0kDn51zvWrVKk2YMEGDBg3SoEGDlJGR8b3/2+A7dn+vv1VRUaGQkBBNnTq1bwcMInbP9cmTJ5Wfn6/4+Hg5HA6NHDmS/470kN1zXVpaqquvvlr9+vWTy+XSnDlz9PXXX/tp2sC1fft2ZWVlKSEhQSEhIef923Dfqq6u1g033CCHw6Err7xSa9eu7dshrSBSUVFhRUREWGvWrLH+/e9/WzNnzrQGDhxoNTU1dbn+o48+ssLCwqxnn33W+uSTT6x58+ZZl1xyibVnzx4/Tx547J7radOmWWVlZdbu3butvXv3Wr/97W8tp9Np/ec///Hz5IHH7rn+1qFDh6whQ4ZYEyZMsH75y1/6Z9gAZ/dct7W1WWPHjrUmT55sffjhh9ahQ4es6upqq66uzs+TBx675/qVV16xHA6H9corr1iHDh2y3nnnHSs+Pt6aM2eOnycPPFu2bLHmzp1rvf7665Yk64033jjv+vr6euvSSy+1CgoKrE8++cR67rnnrLCwMGvr1q19NmNQxci4ceOs/Px8388dHR1WQkKCVVJS0uX6e+65x5oyZUqnfWlpadbvf//7Pp0zGNg91//r7Nmz1oABA6yXX365r0YMGr0512fPnrVuvvlm68UXX7Ryc3OJkR6ye65feOEFa/jw4VZ7e7u/Rgwads91fn6+dfvtt3faV1BQYI0fP75P5ww2PYmRxx57zLr++us77cvOzrYyMzP7bK6geZmmvb1dtbW1ysjI8O0LDQ1VRkaGampqujympqam03pJyszM7HY9vtGbc/2/Tp8+rTNnzmjw4MF9NWZQ6O25/tOf/qSYmBjdf//9/hgzKPTmXL/55ptKT09Xfn6+YmNjNWrUKC1evFgdHR3+Gjsg9eZc33zzzaqtrfW9lFNfX68tW7Zo8uTJfpn5YmLiufFH+Vd7e+P48ePq6OjwfRPst2JjY7Vv374uj3G73V2ud7vdfTZnMOjNuf5fjz/+uBISEs75hUdnvTnXH374oVavXq26ujo/TBg8enOu6+vr9f777+u+++7Tli1bdPDgQT300EM6c+aMiouL/TF2QOrNuZ42bZqOHz+uW265RZZl6ezZs5o1a5aeeOIJf4x8UenuudHj8eirr75Sv379Lvh9Bs2VEQSOJUuWqKKiQm+88YYiIyNNjxNUWlpaNH36dK1atUrR0dGmxwl6Xq9XMTExWrlypVJTU5Wdna25c+eqvLzc9GhBp7q6WosXL9aKFSu0a9cuvf7666qsrNSiRYtMj4YLIGiujERHRyssLExNTU2d9jc1NSkuLq7LY+Li4mytxzd6c66/tXTpUi1ZskTvvfeexowZ05djBgW75/qzzz7T4cOHlZWV5dvn9XolSeHh4dq/f79GjBjRt0MHqN78XsfHx+uSSy5RWFiYb9+1114rt9ut9vZ2RURE9OnMgao353r+/PmaPn26ZsyYIUkaPXq0Wltb9cADD2ju3Lmd/iYafpjunhujoqL65KqIFERXRiIiIpSamqqqqirfPq/Xq6qqKqWnp3d5THp6eqf1kvTuu+92ux7f6M25lqRnn31WixYt0tatWzV27Fh/jBrw7J7ra665Rnv27FFdXZ1vu+uuuzRx4kTV1dXJ5XL5c/yA0pvf6/Hjx+vgwYO+4JOkAwcOKD4+nhA5j96c69OnT58THN9GoMWfWLugjDw39tlbYw2oqKiwHA6HtXbtWuuTTz6xHnjgAWvgwIGW2+22LMuypk+fbhUWFvrWf/TRR1Z4eLi1dOlSa+/evVZxcTEf7e0hu+d6yZIlVkREhLVp0ybr6NGjvq2lpcXUQwgYds/1/+LTND1n91w3NDRYAwYMsB5++GFr//791ttvv23FxMRYTz31lKmHEDDsnuvi4mJrwIAB1quvvmrV19db27Zts0aMGGHdc889ph5CwGhpabF2795t7d6925JkLV++3Nq9e7f1+eefW5ZlWYWFhdb06dN967/9aO8f//hHa+/evVZZWRkf7bXrueees4YOHWpFRERY48aNs/7xj3/4/u3WW2+1cnNzO61/7bXXrJEjR1oRERHW9ddfb1VWVvp54sBl51xfccUVlqRztuLiYv8PHoDs/l7/f8SIPXbP9ccff2ylpaVZDofDGj58uPX0009bZ8+e9fPUgcnOuT5z5oz15JNPWiNGjLAiIyMtl8tlPfTQQ9Z///tf/w8eYD744IMu//v77fnNzc21br311nOOSU5OtiIiIqzhw4dbL730Up/OGGJZXN8CAADmBM17RgAAQGAiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARv0f6ddPEH6xwNQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(p_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe26cd6f-05f9-4172-8ae3-fdce27d9b766",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "y = [0,1,2,3,4,5,6,7,8,9,10]\n",
    "np.random.seed(0)\n",
    "np.random.shuffle(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "557a394e-2689-4a77-b50e-21b909b4a54c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_ = y[:6]\n",
    "np.random.shuffle(y_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7979e1b5-75c4-46a9-8afd-e334ce9ca6f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 0, 3, 8, 10, 6]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32f2148a-d606-43de-bec3-f5673d8a9876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 9, 2, 10, 6, 1, 7, 8, 3, 0, 5]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a60930bd-8af6-4ccb-ab8c-be8d59767a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(len(y)*0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7751b192-32e3-4611-a768-c84c27a9183a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.ones((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "afa38cd2-7d9f-4149-92e9-6b2931d63ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[[1,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52174e17-5838-4128-95e4-c2d8ecf425cb",
   "metadata": {},
   "source": [
    "## Todos\n",
    "1. git issue: p value\n",
    "2. Git issue: morf save space\n",
    "3. do ting thing with more data\n",
    "4. do it on primates\n",
    "5. bonferroni holm correction\n",
    "6. check morf feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b1075d7f-a493-4b5d-a270-394a0b912dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1749353,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "080f4179-9d33-4b19-9b35-db707484ec29",
   "metadata": {},
   "source": [
    "### Things that can be done\n",
    "\n",
    "1. find a one-to-one mapping for mri to compress\n",
    "2. find the slices that are important (any slice > chance is important) and only use them"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908753de-5b4d-43b4-98bd-28b8c8ccb088",
   "metadata": {},
   "source": [
    "### Todos\n",
    "1. Do the PR\n",
    "2. Do primates\n",
    "3. profile the code\n",
    "4. Check splitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54972a6d-a19d-40bd-b766-0472a42a2b29",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21bcf4e0-9932-4453-8ec7-bf94cea95fe4",
   "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
}