image CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import Dataset\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data.sampler import SubsetRandomSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nibabel as nib\n",
    "from scipy import ndimage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import csv\n",
    "# To unpack ADNI data\n",
    "import pickle\n",
    "import time\n",
    "import math\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import network\n",
    "import sys\n",
    "sys.path.insert(1, 'C:\\\\python_data\\\\Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:\\\\python_data',\n",
       " 'C:\\\\python_data\\\\Data',\n",
       " 'C:\\\\anaconda3\\\\envs\\\\pytorch\\\\python38.zip',\n",
       " 'C:\\\\anaconda3\\\\envs\\\\pytorch\\\\DLLs',\n",
       " 'C:\\\\anaconda3\\\\envs\\\\pytorch\\\\lib',\n",
       " 'C:\\\\anaconda3\\\\envs\\\\pytorch',\n",
       " '',\n",
       " 'C:\\\\anaconda3\\\\envs\\\\pytorch\\\\lib\\\\site-packages',\n",
       " 'C:\\\\anaconda3\\\\envs\\\\pytorch\\\\lib\\\\site-packages\\\\win32',\n",
       " 'C:\\\\anaconda3\\\\envs\\\\pytorch\\\\lib\\\\site-packages\\\\win32\\\\lib',\n",
       " 'C:\\\\anaconda3\\\\envs\\\\pytorch\\\\lib\\\\site-packages\\\\Pythonwin',\n",
       " 'C:\\\\anaconda3\\\\envs\\\\pytorch\\\\lib\\\\site-packages\\\\IPython\\\\extensions',\n",
       " 'C:\\\\Users\\\\esthe\\\\.ipython']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from network import Network\n",
    "from data_loader import MRIData\n",
    "import argparse\n",
    "import easydict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dimensions of neuroimages after resizing\n",
    "STANDARD_DIM1 = 200\n",
    "STANDARD_DIM2 = 200\n",
    "STANDARD_DIM3 = 150\n",
    "\n",
    "# Maximum number of images per patient\n",
    "MAX_NUM_IMAGES = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MRIData(Dataset):\n",
    "    \"\"\"\n",
    "    MRI data\n",
    "    The dictionaries AD_Img_Dict.pkl and MCI_Img_Dict.pkl contain key-value\n",
    "      pairs of the following:\n",
    "      key:      subject ID\n",
    "      value:    paths to relevant images (i.e. multiple images per subject)\n",
    "    These dictionaries are converted to arrays and passed into this dataset,\n",
    "    where the paths will be accessed and their neuroimages processed into tensors.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, root_dir, data_array):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            root_dir (string): directory of all the images\n",
    "            data_array (list): array that contains one [key, [value]*] entry for each patient,\n",
    "                               where key:       subject ID\n",
    "                                     value:     paths to patient's MRI .nii neuroimages\n",
    "        \"\"\"\n",
    "        self.root_dir = root_dir\n",
    "        self.data_array = data_array\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        Returns length of dataset       (required by DataLoader)\n",
    "        \"\"\"\n",
    "        return len(self.data_array) # the number of patients in the dataset\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"\n",
    "        Allows indexing of dataset      (required by DataLoader)\n",
    "        Returns a tensor that contains the patient's MRI neuroimages and their diagnoses (AD or MCI)\n",
    "        \"\"\"\n",
    "        \n",
    "        # Get current_patient, where [0] is their ID and [1] is their list of images\n",
    "        current_patient = self.data_array[index]\n",
    "        # List to store the individual image tensors\n",
    "        images_list = []\n",
    "        # The last element in the current patient's array is the classification\n",
    "        patient_label = current_patient.pop()\n",
    "        print(patient_label)\n",
    "        # For each image path, process the .nii image using nibabel\n",
    "        for image_path in current_patient:\n",
    "            print(image_path) #FIXME: delete this\n",
    "            file_name = os.path.join(self.root_dir, image_path)\n",
    "            neuroimage = nib.load(file_name) # Loads proxy image\n",
    "            # Extract the N-D array containing the image data from the nibabel image object\n",
    "            image_data = neuroimage.get_fdata() # Retrieves array data\n",
    "            # Resize and interpolate image\n",
    "            image_size = image_data.shape # Store dimensions of N-D array\n",
    "            current_dim1 = image_size[0]\n",
    "            current_dim2 = image_size[1]\n",
    "            current_dim3 = image_size[2]\n",
    "            # Calculate scale factor for each direction\n",
    "            scale_factor1 = STANDARD_DIM1 / float(current_dim1)\n",
    "            scale_factor2 = STANDARD_DIM2 / float(current_dim2)\n",
    "            scale_factor3 = STANDARD_DIM3 / float(current_dim3)\n",
    "            # Resize image (spline interpolation)\n",
    "            image_data = ndimage.zoom(image_data, (scale_factor1, scale_factor2, scale_factor3))\n",
    "            print(\"Resize success\") #FIXME: delete this\n",
    "            # Convert image data to a tensor\n",
    "            image_data_tensor = torch.Tensor(image_data) \n",
    "            images_list.append(image_data_tensor)\n",
    "        \n",
    "        # Add padding to make all final tensors the same size\n",
    "        num_images = len(images_list)\n",
    "        while (len(images_list) < MAX_NUM_IMAGES):\n",
    "            padding_array = np.zeros((STANDARD_DIM1, STANDARD_DIM2, STANDARD_DIM3))\n",
    "            padding_tensor = torch.Tensor(padding_array)\n",
    "            images_list.append(padding_tensor)\n",
    "\n",
    "        if (len(images_list) > MAX_NUM_IMAGES):\n",
    "            print(\"Error: More than 10 images for one individual patient. Update MAX_NUM_IMAGES in data_loader.py\")\n",
    "\n",
    "        # Convert the list of individual image tensors to a tensor itself\n",
    "        images_tensor = torch.stack(images_list,dim=0)\n",
    "\n",
    "        # Return a dictionary with the images tensor and the label\n",
    "        image_dict = {'images': images_tensor, 'label': patient_label, 'num_images':num_images}\n",
    "\n",
    "        return image_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3145: DtypeWarning: Columns (18,19,20,103,104) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "# Read Diagnostic Data\n",
    "data = pd.read_csv('./input/ADNIMERGE.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We only need ID, Exam Data, Baseline, and Current\n",
    "dx_data = data[['PTID', 'EXAMDATE', 'DX_bl', 'DX']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We only want Diagnoses which have changed\n",
    "m = dx_data.query('DX != DX_bl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to numpy\n",
    "arr = m.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def subject_to_int(val):\n",
    "    return(int(val[0][0:3]) * 1000 + int(val[0][6:10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = sorted(arr, key=subject_to_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If we changed from MCI to AD\n",
    "last = ''\n",
    "count = 0\n",
    "arr_s = []\n",
    "for val in arr:\n",
    "    if val[0] == last:\n",
    "        count += 1\n",
    "    else:\n",
    "        count = 0\n",
    "        last = val[0]\n",
    "    if val[2] == 'LMCI' and val[3] == 'Dementia':\n",
    "        # if this is the case, scan backwards to figure out how many images we have before and add that to val\n",
    "        tmp = list(val)\n",
    "        tmp.append(count)\n",
    "        arr_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only capture the first instance, so the breakthrough Diagnosis\n",
    "arr_final_AD = []\n",
    "last_names = set()\n",
    "for val in arr_s:\n",
    "    if val[0] not in last_names:\n",
    "        arr_final_AD.append(list(val))\n",
    "        last_names.add(val[0])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# measure how many previous visits each patient has\n",
    "count_arr = np.zeros(100)\n",
    "for val in arr_final_AD:\n",
    "    count_arr[val[4]] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# no need to include redundant data\n",
    "for i in range(len(arr_final_AD)):\n",
    "    arr_final_AD[i] = [arr_final_AD[i][0], arr_final_AD[i][1], arr_final_AD[i][4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.DataFrame(arr_final_AD).to_csv(\"./MCI_to_AD.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# capture all patients who started as MCI and remained as MCI\n",
    "# take all patients who started as MCI, and subtract arr_f from it\n",
    "MCI_arr = dx_data.to_numpy()\n",
    "arr_m = []\n",
    "for val in MCI_arr:\n",
    "    if val[2] == 'LMCI':\n",
    "        arr_m.append(list(val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_m = sorted(arr_m, key=subject_to_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# go through each value\n",
    "# if we get dementia or nan, skip everything on the current ID, and do not append\n",
    "# otherwise, when we change, append\n",
    "\n",
    "# x is whatever nan is in the original array. Super hacky, but whatever\n",
    "x = arr_m[4983][3]\n",
    "\n",
    "arr_final_MCI = []\n",
    "last_names = set()\n",
    "last_val = ''\n",
    "count = 0\n",
    "append = True\n",
    "# go through each entry. When the subject ID changes (or is at an end), if there were no instances of dementia, \n",
    "# append that patient ID and the count\n",
    "for val in arr_m:\n",
    "    if val[0] not in last_names:\n",
    "        if append:\n",
    "            tmp = list(last_val)\n",
    "            tmp.append(count)\n",
    "            arr_final_MCI.append(tmp)\n",
    "        last_names.add(val[0])\n",
    "        count = 0\n",
    "        append = True\n",
    "    if val[3] in ['Dementia']:\n",
    "        append = False\n",
    "    last_val = val\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_final_MCI = arr_final_MCI[1::]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0. 54. 62. 20. 18. 24. 23. 39. 25. 10.  7. 14.  5.  8.  7.  7.  6.  6.\n",
      "  4.  4.  2.  4.  0.  0.  0.  0.  0.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "# measure how many previous visits each patient has\n",
    "count_arr = np.zeros(30)\n",
    "for val in arr_final_MCI:\n",
    "    count_arr[val[4]] += 1\n",
    "    \n",
    "print(count_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# no need to include redundant data\n",
    "for i in range(len(arr_final_MCI)):\n",
    "    arr_final_MCI[i] = [arr_final_MCI[i][0], arr_final_MCI[i][1], arr_final_MCI[i][4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Unnamed: 0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>002_S_0729</td>\n",
       "      <td>2007-09-05</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>002_S_0954</td>\n",
       "      <td>2008-06-16</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>002_S_1070</td>\n",
       "      <td>2008-08-12</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>002_S_1268</td>\n",
       "      <td>2013-03-14</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>003_S_1057</td>\n",
       "      <td>2009-03-05</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0           0           1  2\n",
       "0           0  002_S_0729  2007-09-05  2\n",
       "1           1  002_S_0954  2008-06-16  2\n",
       "2           2  002_S_1070  2008-08-12  2\n",
       "3           3  002_S_1268  2013-03-14  9\n",
       "4           4  003_S_1057  2009-03-05  3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file=pd.read_csv('./input/MCI_to_AD.csv')\n",
    "file.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "# 현재 라인의 경로에서 * 모든이름 . *형식자를 모두 files라는곳에 넣어버리겠다는것입니다. \n",
    "# 이때 files는 리스트형식으로 값이 들어간다는것에 유의하시면 좋습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['10. python 시각화2.ipynb',\n",
       " '11. 데이터 전처리.ipynb',\n",
       " '12. KNN.ipynb',\n",
       " '13. naive_bayes (수정).ipynb',\n",
       " '13. naive_bayes.ipynb',\n",
       " '14. Decision_tree.ipynb',\n",
       " '15. clustering.ipynb',\n",
       " '16. RandomForest.ipynb',\n",
       " '6. 통계, 정렬.ipynb',\n",
       " '7. 집계 (Groupby, pivot_table).ipynb',\n",
       " '8. 시계열 데이터(datetime) 소개 및 푸드코트 판매량 분석 및 시각화.ipynb',\n",
       " '9. python 시각화.ipynb',\n",
       " 'anaconda.ipynb',\n",
       " 'card.ipynb',\n",
       " 'clustering.ipynb',\n",
       " 'Combined_MRI_List.pkl',\n",
       " 'CSA.EWHA',\n",
       " 'dacon.ipynb',\n",
       " 'dacon_2.ipynb',\n",
       " 'dacon_r.ipynb',\n",
       " 'dacon_r_최종본.ipynb',\n",
       " 'DataFrame (수정).ipynb',\n",
       " 'DataFrame.ipynb',\n",
       " 'evaluate.py',\n",
       " 'First_HW.ipynb',\n",
       " 'image CNN.ipynb',\n",
       " 'Missing Value.ipynb',\n",
       " 'ML_QUIZ_01.ipynb',\n",
       " 'ML_QUIZ_01_answer.ipynb',\n",
       " 'ML_QUIZ_02.ipynb',\n",
       " 'network.py',\n",
       " 'nii to image.ipynb',\n",
       " 'niicode.py',\n",
       " 'python 기초.ipynb',\n",
       " 'python 기초_answer.ipynb',\n",
       " 'QUIZ_01.ipynb',\n",
       " 'QUIZ_01_answer.ipynb',\n",
       " 'QUIZ_02.ipynb',\n",
       " 'QUIZ_02_answer.ipynb',\n",
       " 'QUIZ_03.ipynb',\n",
       " 'QUIZ_03_answer.ipynb',\n",
       " 'QUIZ_04.ipynb',\n",
       " 'QUIZ_04_answer.ipynb',\n",
       " 'QUIZ_05.ipynb',\n",
       " 'QUIZ_05_answer.ipynb',\n",
       " 'QUIZ_06.ipynb',\n",
       " 'QUIZ_06_answer.ipynb',\n",
       " 'QUIZ_07.ipynb',\n",
       " 'QUIZ_07_answer.ipynb',\n",
       " 'QUIZ_08.ipynb',\n",
       " 'Series.ipynb',\n",
       " 'Series_answer.ipynb',\n",
       " 'Untitled.ipynb',\n",
       " 'Untitled.py',\n",
       " 'Untitled1.ipynb',\n",
       " 'Untitled2.ipynb',\n",
       " 'Untitled3.ipynb',\n",
       " 'Untitled3.py',\n",
       " 'Untitled4.ipynb',\n",
       " 'Untitled5.ipynb',\n",
       " 'Untitled6.ipynb',\n",
       " 'Untitled7.ipynb',\n",
       " 'Untitled9.ipynb',\n",
       " '~$머신러닝_Decision Tree.pptx',\n",
       " '계층 색인.ipynb',\n",
       " '데이터 합치기(merge, concat).ipynb',\n",
       " '머신러닝_Clustering.pptx',\n",
       " '머신러닝_Decision Tree.pptx',\n",
       " '머신러닝_KNN.pptx',\n",
       " '머신러닝_Naive Bayes.pptx',\n",
       " '머신러닝_RandomForest.pptx',\n",
       " '머신러닝_RandomForest_수정.pptx']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = glob.glob('*.*')\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모든 경로를 뽑아내는것입니다.\n",
    "for x in files:\n",
    "    if os.path.isdir(x):\n",
    "        print(x)\n",
    "    else:\n",
    "        print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./Data/MCI_to_AD\\\\022_S_1394\\\\MIDAS_Whole_Brain_Mask\\\\2007-05-29_14_24_28.0\\\\S34317\\\\ADNI_022_S_1394_MR_MIDAS_Whole_Brain_Mask_Br_20120814182221239_S34317_I323573.nii',\n",
       " './Data/MCI_to_AD\\\\022_S_1394\\\\MIDAS_Whole_Brain_Mask\\\\2007-12-14_12_51_19.0\\\\S44027\\\\ADNI_022_S_1394_MR_MIDAS_Whole_Brain_Mask_Br_20120728171651989_S44027_I319376.nii',\n",
       " './Data/MCI_to_AD\\\\022_S_1394\\\\MIDAS_Whole_Brain_Mask\\\\2008-05-27_11_20_32.0\\\\S50642\\\\ADNI_022_S_1394_MR_MIDAS_Whole_Brain_Mask_Br_20120731201713501_S50642_I319971.nii',\n",
       " './Data/MCI_to_AD\\\\022_S_1394\\\\MIDAS_Whole_Brain_Mask\\\\2009-01-22_15_17_27.0\\\\S62182\\\\ADNI_022_S_1394_MR_MIDAS_Whole_Brain_Mask_Br_20120727154223861_S62182_I318854.nii',\n",
       " './Data/MCI_to_AD\\\\022_S_1394\\\\MIDAS_Whole_Brain_Mask\\\\2009-06-29_12_10_46.0\\\\S69282\\\\ADNI_022_S_1394_MR_MIDAS_Whole_Brain_Mask_Br_20120723223311247_S69282_I317519.nii',\n",
       " './Data/MCI_to_AD\\\\022_S_1394\\\\MIDAS_Whole_Brain_Mask\\\\2010-06-28_10_22_03.0\\\\S88279\\\\ADNI_022_S_1394_MR_MIDAS_Whole_Brain_Mask_Br_20120725121950289_S88279_I318246.nii']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = [f for f in glob.glob('./Data/MCI_to_AD/' + \"**/*.nii\", recursive=True)]\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = []\n",
    "for file in files:\n",
    "    split.append(file.split('\\\\'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['./Data/MCI_to_AD',\n",
       "  '022_S_1394',\n",
       "  'MIDAS_Whole_Brain_Mask',\n",
       "  '2007-05-29_14_24_28.0',\n",
       "  'S34317',\n",
       "  'ADNI_022_S_1394_MR_MIDAS_Whole_Brain_Mask_Br_20120814182221239_S34317_I323573.nii']]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize dictionaries\n",
    "image_dict = {}\n",
    "date_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is for MCI_to_AD\n",
    "with open('./input/MCI_to_AD.csv', newline='') as csvfile:\n",
    "    data = list(csv.reader(csvfile))\n",
    "    #temp = csv.reader(csvfile)\n",
    "    #header = next(temp)\n",
    "    #for row in temp:\n",
    "        #data = list(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['', '0', '1', '2'],\n",
       " ['0', '002_S_0729', '2007-09-05', '2'],\n",
       " ['1', '002_S_0954', '2008-06-16', '2'],\n",
       " ['2', '002_S_1070', '2008-08-12', '2'],\n",
       " ['3', '002_S_1268', '2013-03-14', '9']]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for val in data[1::]:\n",
    "    date_dict[val[1]] = val[2]\n",
    "    image_dict[val[1]] = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'002_S_0729': '2007-09-05',\n",
       " '002_S_0954': '2008-06-16',\n",
       " '002_S_1070': '2008-08-12',\n",
       " '002_S_1268': '2013-03-14',\n",
       " '003_S_1057': '2009-03-05',\n",
       " '003_S_1074': '2016-03-29',\n",
       " '005_S_0222': '2007-09-24',\n",
       " '005_S_0448': '2015-04-28',\n",
       " '005_S_0572': '2007-07-09',\n",
       " '002_S_4171': '2013-10-29',\n",
       " '005_S_1224': '2010-03-09',\n",
       " '002_S_4521': '2016-03-24',\n",
       " '006_S_0675': '2008-12-10',\n",
       " '002_S_4746': '2015-08-27',\n",
       " '007_S_0041': '2007-05-14',\n",
       " '007_S_0101': '2007-12-12',\n",
       " '007_S_0128': '2007-08-20',\n",
       " '006_S_1130': '2008-07-01',\n",
       " '007_S_0249': '2010-04-14',\n",
       " '007_S_0293': '2008-03-24',\n",
       " '007_S_0344': '2007-04-25',\n",
       " '007_S_0698': '2015-08-31',\n",
       " '009_S_1030': '2010-12-08',\n",
       " '010_S_0161': '2007-03-30',\n",
       " '006_S_4346': '2015-11-12',\n",
       " '006_S_4363': '2014-12-18',\n",
       " '006_S_4515': '2014-03-10',\n",
       " '010_S_0904': '2009-01-28',\n",
       " '011_S_0241': '2009-03-31',\n",
       " '011_S_0326': '2009-03-30',\n",
       " '011_S_0362': '2009-04-07',\n",
       " '011_S_0856': '2007-03-29',\n",
       " '011_S_0861': '2008-03-25',\n",
       " '011_S_1080': '2010-12-15',\n",
       " '006_S_6252': '2019-05-06',\n",
       " '011_S_1282': '2008-02-19',\n",
       " '012_S_1033': '2007-05-31',\n",
       " '013_S_0240': '2007-10-31',\n",
       " '012_S_1292': '2007-09-12',\n",
       " '009_S_4324': '2016-02-10',\n",
       " '013_S_0325': '2007-12-13',\n",
       " '009_S_4741': '2016-08-24',\n",
       " '013_S_0860': '2007-09-25',\n",
       " '013_S_1186': '2015-03-18',\n",
       " '014_S_0563': '2009-07-21',\n",
       " '014_S_0658': '2007-08-14',\n",
       " '011_S_4366': '2012-11-07',\n",
       " '012_S_4094': '2012-01-25',\n",
       " '016_S_0702': '2008-08-21',\n",
       " '016_S_0769': '2009-08-24',\n",
       " '016_S_1117': '2010-06-24',\n",
       " '016_S_1121': '2008-06-23',\n",
       " '016_S_1138': '2009-01-22',\n",
       " '016_S_1326': '2009-04-01',\n",
       " '013_S_4595': '2015-04-28',\n",
       " '018_S_0057': '2007-08-21',\n",
       " '014_S_4058': '2013-07-09',\n",
       " '012_S_6073': '2019-09-30',\n",
       " '014_S_4079': '2013-07-10',\n",
       " '018_S_0080': '2010-06-08',\n",
       " '018_S_0142': '2016-02-10',\n",
       " '018_S_0155': '2009-03-20',\n",
       " '014_S_4263': '2015-02-10',\n",
       " '018_S_0406': '2009-07-08',\n",
       " '018_S_0450': '2009-07-01',\n",
       " '014_S_4668': '2013-05-23',\n",
       " '016_S_4584': '2014-03-31',\n",
       " '016_S_4902': '2015-12-16',\n",
       " '021_S_0141': '2007-08-06',\n",
       " '021_S_0231': '2008-03-12',\n",
       " '021_S_0276': '2016-04-04',\n",
       " '021_S_0332': '2006-04-19',\n",
       " '021_S_0424': '2010-05-07',\n",
       " '021_S_0626': '2015-08-11',\n",
       " '022_S_0750': '2008-03-07',\n",
       " '023_S_0030': '2006-04-13',\n",
       " '023_S_0042': '2006-11-09',\n",
       " '023_S_0078': '2007-07-24',\n",
       " '022_S_1097': '2010-12-20',\n",
       " '023_S_0126': '2009-03-03',\n",
       " '023_S_0217': '2010-03-12',\n",
       " '019_S_4293': '2013-11-06',\n",
       " '023_S_0331': '2009-04-10',\n",
       " '022_S_1351': '2011-04-12',\n",
       " '023_S_0376': '2010-05-14',\n",
       " '023_S_0388': '2007-10-30',\n",
       " '022_S_1394': '2008-05-27',\n",
       " '023_S_0604': '2007-12-11',\n",
       " '023_S_0625': '2007-02-27',\n",
       " '023_S_0855': '2007-08-06',\n",
       " '023_S_0887': '2008-03-27',\n",
       " '023_S_1126': '2007-12-04',\n",
       " '023_S_1247': '2008-01-29',\n",
       " '024_S_1393': '2008-10-06',\n",
       " '021_S_4402': '2014-12-08',\n",
       " '021_S_4857': '2016-07-29',\n",
       " '023_S_4035': '2015-10-26',\n",
       " '023_S_4115': '2020-01-13',\n",
       " '027_S_0179': '2007-09-10',\n",
       " '023_S_4243': '2014-02-26',\n",
       " '027_S_0256': '2006-10-04',\n",
       " '027_S_0408': '2010-05-10',\n",
       " '027_S_0461': '2007-12-17',\n",
       " '023_S_4502': '2016-04-12',\n",
       " '023_S_4796': '2015-10-09',\n",
       " '027_S_0835': '2008-09-16',\n",
       " '027_S_1213': '2008-01-21',\n",
       " '027_S_1387': '2008-09-11',\n",
       " '029_S_0878': '2008-10-30',\n",
       " '029_S_0914': '2016-01-15',\n",
       " '029_S_1073': '2009-12-17',\n",
       " '029_S_1318': '2013-03-14',\n",
       " '031_S_0294': '2008-03-27',\n",
       " '031_S_0568': '2007-06-07',\n",
       " '027_S_4729': '2015-06-18',\n",
       " '027_S_4757': '2014-06-25',\n",
       " '031_S_0830': '2010-09-23',\n",
       " '027_S_4873': '2016-12-02',\n",
       " '027_S_4936': '2015-10-07',\n",
       " '027_S_4943': '2013-05-01',\n",
       " '031_S_1066': '2008-06-05',\n",
       " '032_S_0187': '2008-02-28',\n",
       " '032_S_0214': '2008-03-21',\n",
       " '032_S_0978': '2008-12-11',\n",
       " '033_S_0511': '2008-06-04',\n",
       " '033_S_0513': '2009-06-03',\n",
       " '033_S_0514': '2010-06-08',\n",
       " '033_S_0567': '2006-12-04',\n",
       " '033_S_0723': '2007-02-16',\n",
       " '033_S_0725': '2008-02-20',\n",
       " '033_S_0906': '2008-10-08',\n",
       " '033_S_0922': '2007-10-12',\n",
       " '033_S_1116': '2009-12-08',\n",
       " '031_S_4042': '2013-02-14',\n",
       " '031_S_4203': '2013-09-23',\n",
       " '035_S_0204': '2007-02-22',\n",
       " '035_S_0292': '2010-04-21',\n",
       " '031_S_4721': '2018-11-01',\n",
       " '035_S_0997': '2008-07-17',\n",
       " '036_S_0869': '2008-11-17',\n",
       " '036_S_0945': '2010-12-10',\n",
       " '036_S_0976': '2010-02-04',\n",
       " '036_S_1135': '2009-01-16',\n",
       " '037_S_0150': '2017-09-05',\n",
       " '037_S_0182': '2006-09-11',\n",
       " '036_S_1240': '2009-03-05',\n",
       " '037_S_0501': '2015-09-16',\n",
       " '037_S_0539': '2008-06-27',\n",
       " '037_S_0552': '2012-08-15',\n",
       " '037_S_0566': '2009-11-23',\n",
       " '037_S_0588': '2012-07-25',\n",
       " '037_S_1078': '2011-01-21',\n",
       " '037_S_1225': '2008-09-08',\n",
       " '035_S_4114': '2015-08-25',\n",
       " '035_S_4414': '2016-06-10',\n",
       " '035_S_4582': '2016-06-06',\n",
       " '035_S_4784': '2015-08-18',\n",
       " '036_S_4430': '2016-02-10',\n",
       " '036_S_4538': '2018-12-19',\n",
       " '036_S_4714': '2016-05-23',\n",
       " '036_S_4715': '2016-06-06',\n",
       " '036_S_4899': '2013-05-14',\n",
       " '037_S_4015': '2016-04-11',\n",
       " '037_S_4030': '2016-06-01',\n",
       " '041_S_0314': '2009-04-20',\n",
       " '037_S_4432': '2015-03-09',\n",
       " '041_S_0549': '2008-03-05',\n",
       " '041_S_1010': '2009-01-12',\n",
       " '041_S_1412': '2008-07-28',\n",
       " '041_S_1423': '2008-08-13',\n",
       " '041_S_1425': '2010-08-16',\n",
       " '041_S_4138': '2017-08-29',\n",
       " '041_S_4720': '2015-06-25',\n",
       " '051_S_1331': '2008-12-10',\n",
       " '052_S_0671': '2015-07-17',\n",
       " '052_S_0952': '2008-05-13',\n",
       " '052_S_1054': '2008-06-25',\n",
       " '052_S_1346': '2011-03-28',\n",
       " '053_S_0389': '2010-05-05',\n",
       " '053_S_0507': '2007-06-11',\n",
       " '051_S_4929': '2014-10-06',\n",
       " '052_S_4807': '2015-08-04',\n",
       " '052_S_4885': '2016-09-12',\n",
       " '052_S_4945': '2016-01-29',\n",
       " '057_S_0839': '2008-10-15',\n",
       " '057_S_0941': '2007-10-30',\n",
       " '057_S_1007': '2008-11-05',\n",
       " '057_S_1217': '2008-02-12',\n",
       " '057_S_1265': '2010-03-10',\n",
       " '057_S_4888': '2016-08-10',\n",
       " '062_S_1299': '2008-09-16',\n",
       " '067_S_0045': '2007-03-07',\n",
       " '067_S_0077': '2007-02-23',\n",
       " '067_S_0098': '2009-04-02',\n",
       " '067_S_0243': '2007-04-10',\n",
       " '067_S_0336': '2006-12-11',\n",
       " '068_S_0442': '2007-06-20',\n",
       " '068_S_0476': '2008-01-07',\n",
       " '068_S_0872': '2011-10-25',\n",
       " '067_S_4918': '2013-10-30',\n",
       " '073_S_0518': '2008-12-01',\n",
       " '073_S_0909': '2009-12-11',\n",
       " '072_S_4057': '2013-06-11',\n",
       " '072_S_4131': '2012-08-13',\n",
       " '072_S_4462': '2014-12-03',\n",
       " '073_S_4777': '2016-11-16',\n",
       " '082_S_0641': '2008-03-12',\n",
       " '082_S_0832': '2008-10-23',\n",
       " '094_S_0434': '2006-11-10',\n",
       " '094_S_1015': '2007-05-21',\n",
       " '094_S_1398': '2007-11-15',\n",
       " '098_S_0160': '2012-02-28',\n",
       " '094_S_4162': '2016-09-29',\n",
       " '098_S_0269': '2007-10-05',\n",
       " '098_S_0667': '2008-07-21',\n",
       " '099_S_0051': '2016-01-18',\n",
       " '099_S_0054': '2006-12-27',\n",
       " '099_S_0111': '2007-02-08',\n",
       " '100_S_0190': '2007-05-08',\n",
       " '100_S_0892': '2009-10-16',\n",
       " '100_S_0930': '2008-11-06',\n",
       " '100_S_0995': '2006-11-13',\n",
       " '100_S_1154': '2007-01-30',\n",
       " '100_S_1226': '2007-08-27',\n",
       " '114_S_0378': '2013-12-10',\n",
       " '114_S_1106': '2013-12-05',\n",
       " '116_S_0361': '2015-05-13',\n",
       " '116_S_0649': '2008-08-06',\n",
       " '116_S_0752': '2007-08-07',\n",
       " '116_S_0834': '2008-04-23',\n",
       " '116_S_1243': '2010-02-24',\n",
       " '116_S_1271': '2009-03-18',\n",
       " '116_S_1315': '2010-04-01',\n",
       " '116_S_4167': '2016-11-16',\n",
       " '121_S_1350': '2007-09-05',\n",
       " '123_S_0050': '2007-06-20',\n",
       " '123_S_0108': '2007-08-09',\n",
       " '123_S_0390': '2008-05-12',\n",
       " '126_S_0708': '2008-07-15',\n",
       " '126_S_0865': '2009-11-19',\n",
       " '126_S_1077': '2008-06-23',\n",
       " '123_S_4096': '2013-08-30',\n",
       " '127_S_0112': '2016-02-05',\n",
       " '127_S_0394': '2007-06-29',\n",
       " '127_S_0925': '2016-11-04',\n",
       " '127_S_1032': '2016-11-30',\n",
       " '128_S_0135': '2010-09-07',\n",
       " '128_S_0227': '2009-09-04',\n",
       " '128_S_0258': '2008-05-08',\n",
       " '127_S_1427': '2008-08-29',\n",
       " '128_S_0611': '2009-10-08',\n",
       " '128_S_0947': '2008-04-17',\n",
       " '128_S_1043': '2009-12-03',\n",
       " '128_S_1148': '2009-09-02',\n",
       " '128_S_1406': '2013-12-10',\n",
       " '128_S_1407': '2010-10-05',\n",
       " '130_S_0285': '2013-05-01',\n",
       " '130_S_0289': '2008-04-03',\n",
       " '130_S_0423': '2008-02-01',\n",
       " '126_S_4458': '2016-02-16',\n",
       " '126_S_4507': '2016-02-01',\n",
       " '126_S_4675': '2016-05-11',\n",
       " '126_S_4712': '2016-05-17',\n",
       " '127_S_4197': '2017-09-15',\n",
       " '127_S_4240': '2014-09-24',\n",
       " '127_S_4928': '2016-09-20',\n",
       " '131_S_1389': '2008-10-30',\n",
       " '132_S_0987': '2008-05-08',\n",
       " '133_S_0638': '2007-09-05',\n",
       " '133_S_0727': '2008-09-24',\n",
       " '133_S_0913': '2008-01-16',\n",
       " '130_S_4250': '2014-10-17',\n",
       " '130_S_4294': '2017-10-18',\n",
       " '130_S_4542': '2016-03-23',\n",
       " '136_S_0195': '2007-03-26',\n",
       " '136_S_0429': '2007-06-18',\n",
       " '136_S_0695': '2009-09-22',\n",
       " '136_S_0873': '2008-04-10',\n",
       " '136_S_0874': '2010-01-15',\n",
       " '137_S_0631': '2007-01-08',\n",
       " '137_S_0973': '2010-12-03',\n",
       " '137_S_0994': '2010-12-09',\n",
       " '135_S_4406': '2014-12-17',\n",
       " '135_S_4689': '2015-05-21',\n",
       " '136_S_4189': '2013-11-06',\n",
       " '137_S_4303': '2013-11-13',\n",
       " '137_S_4596': '2015-04-21',\n",
       " '137_S_4631': '2016-06-14',\n",
       " '141_S_0697': '2010-02-24',\n",
       " '137_S_4815': '2016-09-20',\n",
       " '137_S_4862': '2019-09-10',\n",
       " '141_S_0915': '2008-05-01',\n",
       " '141_S_0982': '2007-11-13',\n",
       " '141_S_1004': '2007-05-11',\n",
       " '141_S_1244': '2007-08-20',\n",
       " '141_S_1255': '2011-03-09',\n",
       " '141_S_4426': '2014-04-07',\n",
       " '168_S_6634': '2020-02-26',\n",
       " '941_S_1295': '2008-08-20',\n",
       " '941_S_1311': '2008-04-23',\n",
       " '941_S_1363': '2007-10-10'}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'067_S_0038'"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entry[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "for entry in split:\n",
    "    # if the image is earlier than the cutoff for the subject\n",
    "    if date_dict[entry[1]] >= entry[3][0:10]:\n",
    "        s = '\\\\'\n",
    "        # add it to the image dict\n",
    "        image_dict[entry[1]].append(s.join(entry))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for each entry, sort by the date\n",
    "def date_int(s):\n",
    "    sp = s.split('\\\\')\n",
    "    date = sp[3][0:10:]\n",
    "    return(10000 * int(date[0:4]) + 100 * int(date[5:7]) + int(date[8::]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "for val in image_dict.keys():\n",
    "    new = sorted(image_dict[val], key=date_int)\n",
    "    image_dict[val] = new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "AD_list = []\n",
    "for key, value in image_dict.items():\n",
    "    tmp = value\n",
    "    if tmp != []:\n",
    "        tmp.append(1)\n",
    "        AD_list.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['./Data/MCI_to_AD\\\\022_S_1394\\\\MIDAS_Whole_Brain_Mask\\\\2007-05-29_14_24_28.0\\\\S34317\\\\ADNI_022_S_1394_MR_MIDAS_Whole_Brain_Mask_Br_20120814182221239_S34317_I323573.nii',\n",
       "  './Data/MCI_to_AD\\\\022_S_1394\\\\MIDAS_Whole_Brain_Mask\\\\2007-12-14_12_51_19.0\\\\S44027\\\\ADNI_022_S_1394_MR_MIDAS_Whole_Brain_Mask_Br_20120728171651989_S44027_I319376.nii',\n",
       "  './Data/MCI_to_AD\\\\022_S_1394\\\\MIDAS_Whole_Brain_Mask\\\\2008-05-27_11_20_32.0\\\\S50642\\\\ADNI_022_S_1394_MR_MIDAS_Whole_Brain_Mask_Br_20120731201713501_S50642_I319971.nii',\n",
       "  1]]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AD_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./Data/MCI_to_MCI\\\\067_S_0038\\\\MIDAS_Whole_Brain_Mask\\\\2005-11-10_09_28_42.0\\\\S10026\\\\ADNI_067_S_0038_MR_MIDAS_Whole_Brain_Mask_Br_20120214125918404_S10026_I284640.nii',\n",
       " './Data/MCI_to_MCI\\\\067_S_0038\\\\MIDAS_Whole_Brain_Mask\\\\2006-06-23_10_05_57.0\\\\S15846\\\\ADNI_067_S_0038_MR_MIDAS_Whole_Brain_Mask_Br_20120822130903789_S15846_I325565.nii',\n",
       " './Data/MCI_to_MCI\\\\067_S_0038\\\\MIDAS_Whole_Brain_Mask\\\\2006-12-07_10_07_31.0\\\\S23553\\\\ADNI_067_S_0038_MR_MIDAS_Whole_Brain_Mask_Br_20120802135733815_S23553_I321093.nii',\n",
       " './Data/MCI_to_MCI\\\\109_S_1343\\\\MIDAS_Whole_Brain_Mask\\\\2007-03-20_14_12_05.0\\\\S28820\\\\ADNI_109_S_1343_MR_MIDAS_Whole_Brain_Mask_Br_20120109011232958_S28820_I276355.nii',\n",
       " './Data/MCI_to_MCI\\\\109_S_1343\\\\MIDAS_Whole_Brain_Mask\\\\2007-10-17_13_30_18.0\\\\S41789\\\\ADNI_109_S_1343_MR_MIDAS_Whole_Brain_Mask_Br_20120802005845136_S41789_I320735.nii',\n",
       " './Data/MCI_to_MCI\\\\109_S_1343\\\\MIDAS_Whole_Brain_Mask\\\\2008-04-18_11_50_39.0\\\\S49500\\\\ADNI_109_S_1343_MR_MIDAS_Whole_Brain_Mask_Br_20120727162213083_S49500_I318894.nii']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is for MCI_to_MCI\n",
    "files = [f for f in glob.glob('./Data/MCI_to_MCI/' + \"**/*.nii\", recursive=True)]\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = []\n",
    "for file in files:\n",
    "    split.append(file.split('\\\\'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['./Data/MCI_to_MCI',\n",
       "  '067_S_0038',\n",
       "  'MIDAS_Whole_Brain_Mask',\n",
       "  '2005-11-10_09_28_42.0',\n",
       "  'S10026',\n",
       "  'ADNI_067_S_0038_MR_MIDAS_Whole_Brain_Mask_Br_20120214125918404_S10026_I284640.nii']]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./input/MCI_to_MCI.csv', newline='') as csvfile:\n",
    "    data = list(csv.reader(csvfile))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['', '0', '1', '2'],\n",
       " ['0', '002_S_1155', '2019-05-13', '20'],\n",
       " ['1', '003_S_0908', '2019-10-29', '20'],\n",
       " ['2', '003_S_1122', '2019-05-07', '19'],\n",
       " ['3', '005_S_0324', '2009-11-23', '8']]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_dict = {}\n",
    "for val in data[1::]:\n",
    "    date_dict[val[1]] = val[2]\n",
    "    image_dict[val[1]] = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'002_S_1155': [], '003_S_0908': []}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(list(image_dict.items())[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "for entry in split:\n",
    "    # if the image is earlier than the cutoff for the subject\n",
    "    s = '\\\\'\n",
    "    # add it to the image dict\n",
    "    image_dict[entry[1]].append(s.join(entry))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'002_S_1155': [], '003_S_0908': []}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(list(image_dict.items())[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = 0\n",
    "for key in image_dict.keys():\n",
    "    if image_dict[key] != []:\n",
    "        count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "MCI_list = []\n",
    "for key, value in image_dict.items():\n",
    "    tmp = value\n",
    "    if tmp != []:\n",
    "        tmp.append(0)\n",
    "        MCI_list.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['./Data/MCI_to_MCI\\\\067_S_0038\\\\MIDAS_Whole_Brain_Mask\\\\2005-11-10_09_28_42.0\\\\S10026\\\\ADNI_067_S_0038_MR_MIDAS_Whole_Brain_Mask_Br_20120214125918404_S10026_I284640.nii',\n",
       "  './Data/MCI_to_MCI\\\\067_S_0038\\\\MIDAS_Whole_Brain_Mask\\\\2006-06-23_10_05_57.0\\\\S15846\\\\ADNI_067_S_0038_MR_MIDAS_Whole_Brain_Mask_Br_20120822130903789_S15846_I325565.nii',\n",
       "  './Data/MCI_to_MCI\\\\067_S_0038\\\\MIDAS_Whole_Brain_Mask\\\\2006-12-07_10_07_31.0\\\\S23553\\\\ADNI_067_S_0038_MR_MIDAS_Whole_Brain_Mask_Br_20120802135733815_S23553_I321093.nii',\n",
       "  0],\n",
       " ['./Data/MCI_to_MCI\\\\109_S_1343\\\\MIDAS_Whole_Brain_Mask\\\\2007-03-20_14_12_05.0\\\\S28820\\\\ADNI_109_S_1343_MR_MIDAS_Whole_Brain_Mask_Br_20120109011232958_S28820_I276355.nii',\n",
       "  './Data/MCI_to_MCI\\\\109_S_1343\\\\MIDAS_Whole_Brain_Mask\\\\2007-10-17_13_30_18.0\\\\S41789\\\\ADNI_109_S_1343_MR_MIDAS_Whole_Brain_Mask_Br_20120802005845136_S41789_I320735.nii',\n",
       "  './Data/MCI_to_MCI\\\\109_S_1343\\\\MIDAS_Whole_Brain_Mask\\\\2008-04-18_11_50_39.0\\\\S49500\\\\ADNI_109_S_1343_MR_MIDAS_Whole_Brain_Mask_Br_20120727162213083_S49500_I318894.nii',\n",
       "  0]]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MCI_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_list = AD_list + MCI_list\n",
    "len(comb_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"./Comb_Img_List.pkl\",\"wb\")\n",
    "pickle.dump(image_dict,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_StoreTrueAction(option_strings=['--disable-cuda'], dest='disable_cuda', nargs=0, const=True, default=False, type=None, choices=None, help='Disable CUDA', metavar=None)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" Unified home for training and evaluation. Imports model and dataloader.\"\"\"\n",
    "\n",
    "parser = argparse.ArgumentParser(description='Train and validate network.')\n",
    "parser.add_argument('--disable-cuda', action='store_true', default=False,\n",
    "                    help='Disable CUDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#args = parser.parse_args()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "args = easydict.EasyDict({'disable-cuda' : False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "args.device = None\n",
    "#print(args.disable_cuda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We aren't using CUDA.\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    print(\"Using CUDA. : )\")\n",
    "    # torch.set_default_tensor_type('torch.cuda.FloatTensor')\n",
    "    args.device = torch.device('cuda')\n",
    "else:\n",
    "    print(\"We aren't using CUDA.\")\n",
    "    args.device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For reproducibility for testing purposes. Delete during actual training.\n",
    "# torch.manual_seed(1)\n",
    "# random.seed(1)\n",
    "\n",
    "## Hyperparameters\n",
    "\n",
    "BATCH_SIZE = 10\n",
    "# Dimensionality of the data outputted by the LSTM,\n",
    "# forwarded to the final dense layer.\n",
    "LSTM_output_size = 16\n",
    "input_size = 1 # Size of the processed MRI scans fed into the CNN.\n",
    "\n",
    "output_dimension = 2 # the number of predictions the model will make\n",
    "# 2 used for binary prediction for each image.\n",
    "# update the splicing used in train()\n",
    "\n",
    "learning_rate = 0.1\n",
    "training_epochs = 5\n",
    "# The size of images passed, as a tuple\n",
    "data_shape = (200,200,150)\n",
    "# Other hyperparameters unlisted: the depth of the model, the kernel size, the padding, the channel restriction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import Data\n",
    "MRI_images_list = pickle.load(open(\"./Combined_MRI_List.pkl\", \"rb\"))\n",
    "random.shuffle(MRI_images_list)\n",
    "\n",
    "train_size = int(0.7 * len(MRI_images_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_x = pickle.load(open('./Comb_Img_List.pkl','rb')) # pkl파일로부터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Data/MCI_to_AD/002_S_0729/MIDAS_Whole_Brain_Mask/2006-07-17_13_27_14.0/S16874/ADNI_002_S_0729_MR_MIDAS_Whole_Brain_Mask_Br_20120731220814445_S16874_I320197.nii',\n",
       "  'Data/MCI_to_AD/002_S_0729/MIDAS_Whole_Brain_Mask/2007-02-22_08_24_49.0/S27092/ADNI_002_S_0729_MR_MIDAS_Whole_Brain_Mask_Br_20120214125224611_S27092_I284619.nii',\n",
       "  'Data/MCI_to_AD/002_S_0729/MIDAS_Whole_Brain_Mask/2007-09-05_06_40_34.0/S38908/ADNI_002_S_0729_MR_MIDAS_Whole_Brain_Mask_Br_20120802125257190_S38908_I321027.nii',\n",
       "  1],\n",
       " ['Data/MCI_to_AD/002_S_0954/MIDAS_Whole_Brain_Mask/2006-10-10_15_13_04.0/S19979/ADNI_002_S_0954_MR_MIDAS_Whole_Brain_Mask_Br_20120814174118495_S19979_I323529.nii',\n",
       "  'Data/MCI_to_AD/002_S_0954/MIDAS_Whole_Brain_Mask/2007-05-03_07_22_07.0/S31734/ADNI_002_S_0954_MR_MIDAS_Whole_Brain_Mask_Br_20120107215225683_S31734_I275942.nii',\n",
       "  'Data/MCI_to_AD/002_S_0954/MIDAS_Whole_Brain_Mask/2007-10-15_13_14_04.0/S41498/ADNI_002_S_0954_MR_MIDAS_Whole_Brain_Mask_Br_20120725055458553_S41498_I318122.nii',\n",
       "  'Data/MCI_to_AD/002_S_0954/MIDAS_Whole_Brain_Mask/2008-06-05_14_14_23.0/S51093/ADNI_002_S_0954_MR_MIDAS_Whole_Brain_Mask_Br_20120731201220904_S51093_I319964.nii',\n",
       "  1],\n",
       " ['Data/MCI_to_AD/002_S_1070/MIDAS_Whole_Brain_Mask/2006-11-28_10_01_04.0/S23120/ADNI_002_S_1070_MR_MIDAS_Whole_Brain_Mask_Br_20120107234225767_S23120_I275982.nii',\n",
       "  'Data/MCI_to_AD/002_S_1070/MIDAS_Whole_Brain_Mask/2006-12-18_09_11_48.0/S24206/ADNI_002_S_1070_MR_MIDAS_Whole_Brain_Mask_Br_20120109172954418_S24206_I276713.nii',\n",
       "  'Data/MCI_to_AD/002_S_1070/MIDAS_Whole_Brain_Mask/2007-06-07_10_30_46.0/S33346/ADNI_002_S_1070_MR_MIDAS_Whole_Brain_Mask_Br_20120106185710857_S33346_I275675.nii',\n",
       "  'Data/MCI_to_AD/002_S_1070/MIDAS_Whole_Brain_Mask/2007-12-10_18_06_07.0/S43524/ADNI_002_S_1070_MR_MIDAS_Whole_Brain_Mask_Br_20120109191832847_S43524_I276828.nii',\n",
       "  1],\n",
       " ['Data/MCI_to_AD/002_S_1268/MIDAS_Whole_Brain_Mask/2007-02-14_07_55_55.0/S26498/ADNI_002_S_1268_MR_MIDAS_Whole_Brain_Mask_Br_20120106193352765_S26498_I275750.nii',\n",
       "  'Data/MCI_to_AD/002_S_1268/MIDAS_Whole_Brain_Mask/2007-09-21_15_04_05.0/S39736/ADNI_002_S_1268_MR_MIDAS_Whole_Brain_Mask_Br_20120731223128184_S39736_I320235.nii',\n",
       "  'Data/MCI_to_AD/002_S_1268/MIDAS_Whole_Brain_Mask/2008-05-22_16_21_48.0/S50532/ADNI_002_S_1268_MR_MIDAS_Whole_Brain_Mask_Br_20120731213900840_S50532_I320153.nii',\n",
       "  'Data/MCI_to_AD/002_S_1268/MIDAS_Whole_Brain_Mask/2008-09-10_09_20_51.0/S56368/ADNI_002_S_1268_MR_MIDAS_Whole_Brain_Mask_Br_20120802012900079_S56368_I320787.nii',\n",
       "  'Data/MCI_to_AD/002_S_1268/MIDAS_Whole_Brain_Mask/2009-03-04_15_42_29.0/S63920/ADNI_002_S_1268_MR_MIDAS_Whole_Brain_Mask_Br_20120727154944714_S63920_I318861.nii',\n",
       "  'Data/MCI_to_AD/002_S_1268/MIDAS_Whole_Brain_Mask/2009-03-04_15_14_55.0/S63925/ADNI_002_S_1268_MR_MIDAS_Whole_Brain_Mask_Br_20120109163546175_S63925_I276662.nii',\n",
       "  'Data/MCI_to_AD/002_S_1268/MIDAS_Whole_Brain_Mask/2010-03-04_12_17_44.0/S80685/ADNI_002_S_1268_MR_MIDAS_Whole_Brain_Mask_Br_20120723185025664_S80685_I317425.nii',\n",
       "  1],\n",
       " ['Data/MCI_to_AD/003_S_1057/MIDAS_Whole_Brain_Mask/2006-12-04_10_43_48.0/S23345/ADNI_003_S_1057_MR_MIDAS_Whole_Brain_Mask_Br_20120725192226814_S23345_I318573.nii',\n",
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       "  'Data/MCI_to_AD/023_S_0126/MIDAS_Whole_Brain_Mask/2007-03-21_12_15_46.0/S28717/ADNI_023_S_0126_MR_MIDAS_Whole_Brain_Mask_Br_20120814113347462_S28717_I323394.nii',\n",
       "  'Data/MCI_to_AD/023_S_0126/MIDAS_Whole_Brain_Mask/2007-08-14_07_55_46.0/S37291/ADNI_023_S_0126_MR_MIDAS_Whole_Brain_Mask_Br_20120723222545028_S37291_I317502.nii',\n",
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       "  1],\n",
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       "  'Data/MCI_to_AD/023_S_0217/MIDAS_Whole_Brain_Mask/2008-02-25_13_47_13.0/S46164/ADNI_023_S_0217_MR_MIDAS_Whole_Brain_Mask_Br_20120802013814189_S46164_I320799.nii',\n",
       "  'Data/MCI_to_AD/023_S_0217/MIDAS_Whole_Brain_Mask/2009-03-06_13_05_54.0/S64059/ADNI_023_S_0217_MR_MIDAS_Whole_Brain_Mask_Br_20120725155402684_S64059_I318349.nii',\n",
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       "  1],\n",
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       "  1],\n",
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       "  1],\n",
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       "  'Data/MCI_to_AD/027_S_1213/MIDAS_Whole_Brain_Mask/2007-07-20_10_29_12.0/S35523/ADNI_027_S_1213_MR_MIDAS_Whole_Brain_Mask_Br_20120731192316698_S35523_I319873.nii',\n",
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       " ['Data/MCI_to_MCI/073_S_1357/MIDAS_Whole_Brain_Mask/2007-04-09_13_40_07.0/S29544/ADNI_073_S_1357_MR_MIDAS_Whole_Brain_Mask_Br_20120725044114789_S29544_I318001.nii',\n",
       "  0],\n",
       " ['Data/MCI_to_MCI/082_S_0469/MIDAS_Whole_Brain_Mask/2006-06-06_08_42_49.0/S15262/ADNI_082_S_0469_MR_MIDAS_Whole_Brain_Mask_Br_20120802105442243_S15262_I320970.nii',\n",
       "  'Data/MCI_to_MCI/082_S_0469/MIDAS_Whole_Brain_Mask/2006-07-17_13_37_53.0/S16916/ADNI_082_S_0469_MR_MIDAS_Whole_Brain_Mask_Br_20120107183711741_S16916_I275868.nii',\n",
       "  0],\n",
       " ['Data/MCI_to_MCI/082_S_0928/MIDAS_Whole_Brain_Mask/2007-10-18_11_34_35.0/S41778/ADNI_082_S_0928_MR_MIDAS_Whole_Brain_Mask_Br_20120108041305186_S41778_I276085.nii',\n",
       "  'Data/MCI_to_MCI/082_S_0928/MIDAS_Whole_Brain_Mask/2008-06-12_13_44_08.0/S51618/ADNI_082_S_0928_MR_MIDAS_Whole_Brain_Mask_Br_20120106190013298_S51618_I275683.nii',\n",
       "  'Data/MCI_to_MCI/082_S_0928/MIDAS_Whole_Brain_Mask/2007-10-11_12_32_39.0/S41395/ADNI_082_S_0928_MR_MIDAS_Whole_Brain_Mask_Br_20120727163119318_S41395_I318914.nii',\n",
       "  'Data/MCI_to_MCI/082_S_0928/MIDAS_Whole_Brain_Mask/2007-06-11_15_08_02.0/S41422/ADNI_082_S_0928_MR_MIDAS_Whole_Brain_Mask_Br_20120731183519995_S41422_I319799.nii',\n",
       "  'Data/MCI_to_MCI/082_S_0928/MIDAS_Whole_Brain_Mask/2006-09-29_11_10_42.0/S19648/ADNI_082_S_0928_MR_MIDAS_Whole_Brain_Mask_Br_20120214130312980_S19648_I284652.nii',\n",
       "  0],\n",
       " ['Data/MCI_to_MCI/082_S_1119/MIDAS_Whole_Brain_Mask/2008-01-17_12_59_09.0/S44687/ADNI_082_S_1119_MR_MIDAS_Whole_Brain_Mask_Br_20120731235831672_S44687_I320373.nii',\n",
       "  'Data/MCI_to_MCI/082_S_1119/MIDAS_Whole_Brain_Mask/2006-12-07_12_55_42.0/S23733/ADNI_082_S_1119_MR_MIDAS_Whole_Brain_Mask_Br_20120106210304414_S23733_I275791.nii',\n",
       "  0],\n",
       " ['Data/MCI_to_MCI/094_S_1293/MIDAS_Whole_Brain_Mask/2007-09-10_09_48_44.0/S39310/ADNI_094_S_1293_MR_MIDAS_Whole_Brain_Mask_Br_20120109044618197_S39310_I276436.nii',\n",
       "  'Data/MCI_to_MCI/094_S_1293/MIDAS_Whole_Brain_Mask/2008-03-03_09_48_25.0/S46532/ADNI_094_S_1293_MR_MIDAS_Whole_Brain_Mask_Br_20120822191628026_S46532_I325791.nii',\n",
       "  'Data/MCI_to_MCI/094_S_1293/MIDAS_Whole_Brain_Mask/2007-03-12_08_28_54.0/S27865/ADNI_094_S_1293_MR_MIDAS_Whole_Brain_Mask_Br_20120723174639122_S27865_I317343.nii',\n",
       "  'Data/MCI_to_MCI/094_S_1293/MIDAS_Whole_Brain_Mask/2008-09-11_12_54_05.0/S56437/ADNI_094_S_1293_MR_MIDAS_Whole_Brain_Mask_Br_20120108152633640_S56437_I276134.nii',\n",
       "  0],\n",
       " ['Data/MCI_to_MCI/099_S_0060/MIDAS_Whole_Brain_Mask/2007-01-11_12_41_05.0/S24972/ADNI_099_S_0060_MR_MIDAS_Whole_Brain_Mask_Br_20120213121746568_S24972_I283973.nii',\n",
       "  'Data/MCI_to_MCI/099_S_0060/MIDAS_Whole_Brain_Mask/2005-12-07_08_58_03.0/S10478/ADNI_099_S_0060_MR_MIDAS_Whole_Brain_Mask_Br_20120108215149487_S10478_I276279.nii',\n",
       "  'Data/MCI_to_MCI/099_S_0060/MIDAS_Whole_Brain_Mask/2006-07-24_07_27_47.0/S17136/ADNI_099_S_0060_MR_MIDAS_Whole_Brain_Mask_Br_20120802103023137_S17136_I320943.nii',\n",
       "  0],\n",
       " ['Data/MCI_to_MCI/099_S_0880/MIDAS_Whole_Brain_Mask/2006-10-05_07_03_06.0/S19744/ADNI_099_S_0880_MR_MIDAS_Whole_Brain_Mask_Br_20120213122738492_S19744_I284010.nii',\n",
       "  'Data/MCI_to_MCI/099_S_0880/MIDAS_Whole_Brain_Mask/2007-12-07_09_37_54.0/S43444/ADNI_099_S_0880_MR_MIDAS_Whole_Brain_Mask_Br_20120802031758474_S43444_I320924.nii',\n",
       "  'Data/MCI_to_MCI/099_S_0880/MIDAS_Whole_Brain_Mask/2007-04-25_07_39_44.0/S30547/ADNI_099_S_0880_MR_MIDAS_Whole_Brain_Mask_Br_20120725154844169_S30547_I318339.nii',\n",
       "  0],\n",
       " ['Data/MCI_to_MCI/099_S_0958/MIDAS_Whole_Brain_Mask/2006-10-20_10_58_17.0/S20493/ADNI_099_S_0958_MR_MIDAS_Whole_Brain_Mask_Br_20120731232254672_S20493_I320317.nii',\n",
       "  0],\n",
       " ['Data/MCI_to_MCI/109_S_1343/MIDAS_Whole_Brain_Mask/2007-03-20_14_12_05.0/S28820/ADNI_109_S_1343_MR_MIDAS_Whole_Brain_Mask_Br_20120109011232958_S28820_I276355.nii',\n",
       "  'Data/MCI_to_MCI/109_S_1343/MIDAS_Whole_Brain_Mask/2007-10-17_13_30_18.0/S41789/ADNI_109_S_1343_MR_MIDAS_Whole_Brain_Mask_Br_20120802005845136_S41789_I320735.nii',\n",
       "  'Data/MCI_to_MCI/109_S_1343/MIDAS_Whole_Brain_Mask/2008-04-18_11_50_39.0/S49500/ADNI_109_S_1343_MR_MIDAS_Whole_Brain_Mask_Br_20120727162213083_S49500_I318894.nii',\n",
       "  0],\n",
       " ['Data/MCI_to_MCI/114_S_0410/MIDAS_Whole_Brain_Mask/2006-04-18_08_18_41.0/S13289/ADNI_114_S_0410_MR_MIDAS_Whole_Brain_Mask_Br_20120214123234917_S13289_I284556.nii',\n",
       "  'Data/MCI_to_MCI/114_S_0410/MIDAS_Whole_Brain_Mask/2006-11-29_14_18_14.0/S23195/ADNI_114_S_0410_MR_MIDAS_Whole_Brain_Mask_Br_20120213131409043_S23195_I284123.nii',\n",
       "  'Data/MCI_to_MCI/114_S_0410/MIDAS_Whole_Brain_Mask/2007-04-23_09_39_19.0/S30133/ADNI_114_S_0410_MR_MIDAS_Whole_Brain_Mask_Br_20120731223907811_S30133_I320248.nii',\n",
       "  0]]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_x = pickle.load(open('./Combined_MRI_List.pkl', 'rb'))\n",
    "new_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split list\n",
    "training_list = MRI_images_list[:train_size]\n",
    "test_list =  MRI_images_list[train_size:]\n",
    "\n",
    "DATA_ROOT_DIR = './'\n",
    "train_dataset = MRIData(DATA_ROOT_DIR, training_list)\n",
    "test_dataset = MRIData(DATA_ROOT_DIR, test_list)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "\n",
    "training_data = train_loader\n",
    "test_data = test_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing hyperparameters...\n",
      "For the specified shape, the LSTM input dimension has been calculated at 320.\n"
     ]
    }
   ],
   "source": [
    "## Define Model\n",
    "model = Network(input_size, data_shape, output_dimension).to(args.device)\n",
    "loss_function = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Training Function\n",
    "def train(model,training_data,optimizer,criterion):\n",
    "    \"\"\" takes (model, training data, optimizer, loss function)\"\"\"\n",
    "    # Activate training mode\n",
    "    model.train()\n",
    "    # Initialize the per epoch loss\n",
    "    epoch_loss = 0\n",
    "    epoch_length = len(training_data)\n",
    "    for i, patient_data in enumerate(training_data):\n",
    "        if i % (math.floor(epoch_length / 5) + 1) == 0: print(f\"\\t\\tTraining Progress:{i / len(training_data) * 100}%\")\n",
    "        # Clear gradients\n",
    "        model.zero_grad()\n",
    "        torch.cuda.empty_cache() # Clear CUDA memory\n",
    "        batch_loss=torch.tensor(0.0).to(args.device)\n",
    "\n",
    "        # Clear the LSTM hidden state after each patient\n",
    "        model.hidden = model.init_hidden()\n",
    "\n",
    "        # Get the MRI's and classifications for the current patient\n",
    "        patient_markers = patient_data['num_images']\n",
    "        patient_MRIs = patient_data[\"images\"].to(args.device)\n",
    "\n",
    "        patient_classifications = patient_data[\"label\"]\n",
    "        print(\"Patient batch classes \", patient_classifications)\n",
    "\n",
    "        for x in range(len(patient_MRIs)):\n",
    "            try:\n",
    "                # Clear hidden states to give each patient a clean slate\n",
    "                model.hidden = model.init_hidden()\n",
    "                single_patient_MRIs = patient_MRIs[x][:patient_markers[x]].view(-1,1,data_shape[0],data_shape[1],data_shape[2])\n",
    "\n",
    "                patient_diagnosis = patient_classifications[x]\n",
    "                patient_endstate = torch.ones(single_patient_MRIs.size(0)) * patient_diagnosis\n",
    "                patient_endstate = patient_endstate.long().to(args.device)\n",
    "\n",
    "                out = model(single_patient_MRIs)\n",
    "\n",
    "                if len(out.shape)==1:\n",
    "                    out = out[None,...] # In the case of a single input, we need padding\n",
    "\n",
    "                print(\"model predictions are \",out)\n",
    "                print(\"patient endstate is \",patient_endstate)\n",
    "                model_predictions = out\n",
    "\n",
    "                loss = criterion(model_predictions, patient_endstate)\n",
    "                batch_loss += loss\n",
    "\n",
    "            except Exception as e:\n",
    "                print(\"EXCEPTION CAUGHT:\",e)\n",
    "\n",
    "        batch_loss.backward()\n",
    "        print(\"batch loss is\",batch_loss)\n",
    "        optimizer.step()\n",
    "        epoch_loss += batch_loss\n",
    "\n",
    "    if epoch_length == 0: epoch_length = 0.000001\n",
    "    return epoch_loss / epoch_length\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Testing Function\n",
    "def test(model, test_data, criterion):\n",
    "    \"\"\"takes (model, test_data, loss function) and returns the epoch loss.\"\"\"\n",
    "    model.eval()\n",
    "    epoch_loss = torch.tensor(0.0)\n",
    "    epoch_length = len(test_data)\n",
    "    for i, patient_data in enumerate(test_data):\n",
    "        if i % (math.floor(epoch_length / 5) + 1) == 0: print(f\"\\t\\tTesting Progress:{i / len(test_data) * 100}%\")\n",
    "        # Clear gradients\n",
    "        model.zero_grad()\n",
    "        torch.cuda.empty_cache() # Clear CUDA memory\n",
    "\n",
    "        # Clear the LSTM hidden state after each patient\n",
    "        model.hidden = model.init_hidden()\n",
    "        # Get the MRI's and classifications for the current patient\n",
    "        patient_markers = patient_data['num_images']\n",
    "        patient_MRIs = patient_data[\"images\"].to(args.device)\n",
    "\n",
    "        patient_classifications = patient_data[\"label\"]\n",
    "        print(\"Patient batch classes \", patient_classifications)\n",
    "        for x in range(len(patient_MRIs)):\n",
    "            try:\n",
    "                # Clear hidden states to give each patient a clean slate\n",
    "                model.hidden = model.init_hidden()\n",
    "                single_patient_MRIs = patient_MRIs[x][:patient_markers[x]].view(-1, 1, data_shape[0], data_shape[1],\n",
    "                                                                                data_shape[2])\n",
    "                single_patient_MRIs = single_patient_MRIs\n",
    "                patient_diagnosis = patient_classifications[x]\n",
    "                patient_endstate = torch.ones(single_patient_MRIs.size(0)) * patient_diagnosis\n",
    "                patient_endstate = patient_endstate.long().to(args.device)\n",
    "\n",
    "                out = model(single_patient_MRIs)\n",
    "\n",
    "                if len(out.shape)==1:\n",
    "                    out = out[None,...] # In the case of a single input, we need padding\n",
    "\n",
    "                model_predictions = out\n",
    "\n",
    "                loss = criterion(model_predictions, patient_endstate)\n",
    "                epoch_loss += loss\n",
    "                print(\"Current test loss \",loss)\n",
    "            except Exception as e:\n",
    "                epoch_length -= 1\n",
    "                print(\"EXCEPTION CAUGHT:\", e)\n",
    "\n",
    "    if epoch_length == 0: epoch_length = 0.000001\n",
    "    return epoch_loss / epoch_length\n",
    "\n",
    "# Perform training and measure test accuracy. Save best performing model.\n",
    "best_test_accuracy = float('inf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Data/MCI_to_MCI/003_S_0908/MIDAS_Whole_Brain_Mask/2006-09-12_16_07_53.0/S32516/ADNI_003_S_0908_MR_MIDAS_Whole_Brain_Mask_Br_20120725051452637_S32516_I318059.nii\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "No such file or no access: './Data/MCI_to_MCI/003_S_0908/MIDAS_Whole_Brain_Mask/2006-09-12_16_07_53.0/S32516/ADNI_003_S_0908_MR_MIDAS_Whole_Brain_Mask_Br_20120725051452637_S32516_I318059.nii'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\nibabel\\loadsave.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(filename, **kwargs)\u001b[0m\n\u001b[0;32m     41\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m         \u001b[0mstat_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     43\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] 지정된 경로를 찾을 수 없습니다: './Data/MCI_to_MCI/003_S_0908/MIDAS_Whole_Brain_Mask/2006-09-12_16_07_53.0/S32516/ADNI_003_S_0908_MR_MIDAS_Whole_Brain_Mask_Br_20120725051452637_S32516_I318059.nii'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-79-c6fa92c50a0a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mstart_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0mtrain_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss_function\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mtest_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss_function\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-73-19a6fd9a0721>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(model, training_data, optimizer, criterion)\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mepoch_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mepoch_length\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpatient_data\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch_length\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"\\t\\tTraining Progress:{i / len(training_data) * 100}%\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[1;31m# Clear gradients\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    362\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 363\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    364\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    401\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    402\u001b[0m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 403\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    404\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\data_loader.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m     63\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#FIXME: delete this\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m             \u001b[0mfile_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mroot_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m             \u001b[0mneuroimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Loads proxy image\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m             \u001b[1;31m# Extract the N-D array containing the image data from the nibabel image object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m             \u001b[0mimage_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mneuroimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_fdata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Retrieves array data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\nibabel\\loadsave.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(filename, **kwargs)\u001b[0m\n\u001b[0;32m     42\u001b[0m         \u001b[0mstat_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"No such file or no access: '{filename}'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mstat_result\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mst_size\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mImageFileError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Empty file: '{filename}'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: No such file or no access: './Data/MCI_to_MCI/003_S_0908/MIDAS_Whole_Brain_Mask/2006-09-12_16_07_53.0/S32516/ADNI_003_S_0908_MR_MIDAS_Whole_Brain_Mask_Br_20120725051452637_S32516_I318059.nii'"
     ]
    }
   ],
   "source": [
    "# This evaluation workflow below was adapted from Ben Trevett's design\n",
    "# on https://github.com/bentrevett/pytorch-seq2seq/blob/master/1%20-%20Sequence%20to%20Sequence%20Learning%20with%20Neural%20Networks.ipynb\n",
    "for epoch in range(training_epochs):\n",
    "\n",
    "    start_time = time.time()\n",
    "\n",
    "    train_loss = train(model, training_data, optimizer, loss_function)\n",
    "    test_loss = test(model, test_data, loss_function)\n",
    "\n",
    "    end_time = time.time()\n",
    "\n",
    "    epoch_mins = math.floor((end_time-start_time)/60)\n",
    "    epoch_secs = math.floor((end_time-start_time)%60)\n",
    "\n",
    "    print(f\"Hurrah! Epoch {epoch + 1}/{training_epochs} concludes. | Time: {epoch_mins}m {epoch_secs}s\")\n",
    "    print(f\"\\tTrain Loss: {train_loss:.3f}| Train Perplexity: {math.exp(train_loss):7.3f}\")\n",
    "    print(f\"\\tTest Loss: {test_loss:.3f}| Test Perplexity: {math.exp(test_loss):7.3f}\")\n",
    "\n",
    "\n",
    "    if test_loss<best_test_accuracy:\n",
    "        print(\"...that was our best test accuracy yet!\")\n",
    "        best_test_accuracy=test_loss\n",
    "        torch.save(model.state_dict(),'ad-model.pt')"
   ]
  }
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