vcog_hpc_prediction_multi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/angela/anaconda3/envs/vcog_paper_py35/lib/python3.5/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n",
      "/home/angela/anaconda3/envs/vcog_paper_py35/lib/python3.5/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import scipy.io\n",
    "from os import listdir\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn import preprocessing\n",
    "#from nilearn import plotting\n",
    "#from proteus.io import util\n",
    "from proteus.visu import sbp_visu\n",
    "\n",
    "import glob,os\n",
    "#import nibabel as nib\n",
    "\n",
    "\n",
    "import pickle\n",
    "from proteus.predic import high_confidence_at\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from proteus.predic import prediction\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "\n",
    "from copy import deepcopy\n",
    "\n",
    "from itertools import cycle\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp\n",
    "\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stats_mask(y_true, y_pred, mask_selected=None):\n",
    "    if mask_selected is None:\n",
    "        mask_selected = np.ones(y_pred.shape).astype(bool)\n",
    "    print('------------------------')\n",
    "    print('Ratio:', y_true[mask_selected].sum()/y_true.sum()) \n",
    "    print('#    : ', y_true[mask_selected].sum()) \n",
    "    print('# true values: ',mask_selected.sum())\n",
    "    print('ACC  : ', np.mean((y_true == y_pred)[mask_selected])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predic_stats(y_, y_pred, lr_decision):\n",
    "    # number of AD subjects\n",
    "    n_ad = sum(y_)\n",
    "    print('Total number of TARGET subjects: ', n_ad)\n",
    "\n",
    "    # number of CN subjects\n",
    "    n_cn = len(y_) - sum(y_)\n",
    "    print('Total number of NON-TARGET subjects: ', n_cn)\n",
    "    \n",
    "    # number of subjects predicted as AD at stage 1\n",
    "    n_pos = sum(y_pred)\n",
    "    print('Stage 1 number of hits (true and false positives): ', n_pos)\n",
    "    \n",
    "    # true positives at stage 1\n",
    "    n_pos_ad = sum(y_pred[y_.astype(bool)])\n",
    "    print('Stage 1 TRUE positives: ', n_pos_ad)\n",
    "    \n",
    "    # false positives at stage 1\n",
    "    n_pos_cn = n_pos - n_pos_ad\n",
    "    print('Stage 1 FALSE positives: ', n_pos_cn)\n",
    "    \n",
    "    # number of CN subjects not identified as positive (true negatives)\n",
    "    n_neg1_cn = n_cn - n_pos_cn\n",
    "    print('Stage 1 TRUE negatives: ', n_neg1_cn)\n",
    "\n",
    "    # number of all flagged HPC-AD subjects\n",
    "    n_flag = sum(y_pred[lr_decision>0])\n",
    "    print('Total number of flagged HPC-AD subjects: ', n_flag)\n",
    "\n",
    "    # number of flagged HPC-AD subjects who are actually AD (true positives)\n",
    "    y_pred_true = y_ + y_pred\n",
    "    y_pred_true = y_pred_true==2\n",
    "    n_flag_ad = sum(y_pred_true[lr_decision>0])\n",
    "    print('Number of flagged HPC-AD subjects that are TRUE positives: ', n_flag_ad)\n",
    "\n",
    "    # number of flagged HPC-AD subjects that are actually CN (false positives)\n",
    "    n_flag_cn = n_flag - n_flag_ad\n",
    "    print('Number of flagged HPC-AD subjects that are FALSE positives: ', n_flag_cn)\n",
    "\n",
    "    # number of CN subjects that were not flagged (true negatives)\n",
    "    n_neg_cn = n_cn - n_flag_cn\n",
    "    print('Number of true negatives: ', n_neg_cn)\n",
    "    \n",
    "    print('#############################')\n",
    "    print('Stage 1 stats for TARGET vs NON-TARGET')\n",
    "    print('Precision for AD: ', n_pos_ad/(n_pos_ad + n_pos_cn))\n",
    "    prec = n_pos_ad/(n_pos_ad + n_pos_cn)\n",
    "    print('Recall (or sensitivity)  for AD: ', n_pos_ad/n_ad)\n",
    "    sens = n_pos_ad/n_ad\n",
    "    print('Specificity: ', n_neg1_cn/n_cn)\n",
    "    spec = n_neg1_cn/n_cn\n",
    "    fp = (1-spec)*664\n",
    "    tp = sens*336\n",
    "    adj_prec = tp/(tp+fp)\n",
    "    print('Adjusted precision for 33.6% baseline rate: ', adj_prec)\n",
    "    print('Accuracy: ', (n_pos_ad + n_neg1_cn)/(n_ad + n_cn))\n",
    "    acc = (n_pos_ad + n_neg1_cn)/(n_ad + n_cn)\n",
    "\n",
    "    print('#############################')\n",
    "    print('Stage 2 stats for TARGET vs NON-TARGET')\n",
    "    print('Precision for HPC-AD: ', n_flag_ad/n_flag)\n",
    "    prec_2 = n_flag_ad/n_flag\n",
    "    print('Recall (or sensitivity) for HPC-AD: ', n_flag_ad/n_ad)\n",
    "    sens_2 = n_flag_ad/n_ad\n",
    "    print('Specificity: ', n_neg_cn/n_cn)\n",
    "    spec_2 = n_neg_cn/n_cn\n",
    "    fp_2 = (1-spec_2)*664\n",
    "    tp_2 = sens_2*336\n",
    "    adj_prec_2 = tp_2/(tp_2 + fp_2)\n",
    "    print('Adjusted precision for 33.6% baseline rate: ', adj_prec_2)\n",
    "    print('Accuracy: ', (n_flag_ad + n_neg_cn)/(n_ad + n_cn))\n",
    "    acc_2 = (n_flag_ad + n_neg_cn)/(n_ad + n_cn)\n",
    "    \n",
    "    return sens, spec, prec, acc, sens_2, spec_2, prec_2, acc_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale=2)\n",
    "sns.set_style(\"white\")\n",
    "#sns.set_context(\"paper\")\n",
    "#sns.set_palette(\"colorblind\")\n",
    "#sns.set_palette(\"GnBu_d\")\n",
    "\n",
    "#sns.set_palette(sns.cubehelix_palette(n_colors=8))\n",
    "#sns.set_palette(sns.color_palette(\"BrBG\", 6))\n",
    "\n",
    "cpal = [\"#F0DFB2\", \"#CFA255\", \"#995D12\", \"#B3E2DB\", \"#58B0A6\", \"#0D7068\"]\n",
    "sns.set_palette(cpal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_results = '/home/angela/Desktop/vcog_paper/gigascience/third_submission/roc/vcog/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load longitudinal data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "#np.random.RandomState(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/angela/anaconda3/envs/vcog_paper_py35/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2785: DtypeWarning: Columns (101) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "long_data = pd.read_csv('/home/angela/Documents/adni_csv/adnimerge_upenn_unw_av45_neurobat.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data for the training set (ADNI1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('/home/angela/Desktop/vcog_paper/adni1_vbm_adcn_subtypes_20171209/7clus/adni1_model_weights.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(subset=['sub1','age_scan','gender','mean_gm','tiv', 'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR'],\n",
    "            inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[['RID','age_scan','gender','mean_gm','tiv',\n",
    "             'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR',\n",
    "             'sub1','sub2','sub3','sub4','sub5','sub6','sub7',\n",
    "             'ABETA','TAU','conv_2_ad','AD','MCI','CN','APOE4_bin','DX']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Organize the AD & CN data for classification task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Mask of the CN and AD subjects only\n",
    "mask_cnad = data.loc[:,['CN','AD']].values.sum(1).astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((370, 16), (370,), (370, 4))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reload(high_confidence)\n",
    "scalerX = preprocessing.StandardScaler()\n",
    "scaler = preprocessing.StandardScaler()\n",
    "\n",
    "x_ = data.iloc[mask_cnad,data.columns.get_loc(\"ADAS13\"):data.columns.get_loc(\"sub7\")+1].values\n",
    "#x_ = scalerX.fit_transform(x_)\n",
    "y_ = data[['AD']].values.ravel()[mask_cnad]\n",
    "\n",
    "confounds = data[['gender','age_scan','mean_gm','tiv']].values[mask_cnad,:]\n",
    "#confounds = data[['sex','age_r']].values[mask_cnad,:]\n",
    "#confounds[:, 1:] = scaler.fit_transform(confounds[:, 1:])\n",
    "#confounds[:, 0] = preprocessing.binarize(confounds[:, 0].reshape(-1, 1), threshold=1)[:, 0]\n",
    "\n",
    "#crm = prediction.ConfoundsRm(confounds, x_)\n",
    "#x_ = crm.transform(confounds, x_)\n",
    "\n",
    "x_ = scaler.fit_transform(np.hstack((x_,confounds)))\n",
    "#x_ = np.hstack((x_,confounds))\n",
    "\n",
    "\n",
    "x_.shape, y_.shape, confounds.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross-validation HPC in ADNI1 AD & CN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stage 1\n",
      "Proba:\n",
      "[1.         1.         1.         1.         1.         0.95435685\n",
      " 1.         1.         1.         1.         0.77290837 1.\n",
      " 0.99215686 0.99607843 1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         0.99227799 1.\n",
      " 1.         0.97619048 1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         0.9707113  1.\n",
      " 0.60080645 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         0.98473282\n",
      " 1.         1.         0.99596774 1.         1.         1.\n",
      " 1.         1.         1.         1.         0.99601594 0.86055777\n",
      " 1.         1.         0.49794239 1.         1.         1.\n",
      " 1.         1.         0.93181818 0.21011673 1.         1.\n",
      " 1.         0.90987124 0.97177419 1.         0.05839416 1.\n",
      " 0.72083333 0.91139241 0.87346939 1.         1.         1.\n",
      " 1.         1.         0.59683794 1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         0.64822134 1.         1.         1.\n",
      " 1.         1.         0.43253968 1.         1.         1.\n",
      " 1.         0.99576271 1.         1.         0.99628253 1.\n",
      " 1.         1.         1.         0.99236641 1.         1.\n",
      " 0.87815126 0.968      1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.99604743 1.         1.         1.         0.69583333 1.\n",
      " 1.         1.         1.         1.         0.87250996 1.\n",
      " 0.87755102 0.988      1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         0.97021277 1.\n",
      " 1.         1.         1.         1.         1.         0.98841699\n",
      " 1.         0.97413793 1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         0.99603175\n",
      " 0.98755187 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         0.91153846 0.9877551  1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.        ]\n",
      "Average hm score 0.8333333333333334\n",
      "Stage 2\n",
      "Adjusted gamma:  1.0\n",
      "Adjusted gamma:  1.0\n",
      "Classifying AD vs CN...\n",
      "[0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.\n",
      " 0. 1. 0. 1. 1. 0. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0.\n",
      " 0. 1. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0.\n",
      " 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0.\n",
      " 1. 0. 1. 1. 1. 1. 0. 1. 0. 0. 1. 0. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1.\n",
      " 1. 0. 1. 1.]\n",
      "Total number of TARGET subjects:  55.0\n",
      "Total number of NON-TARGET subjects:  69.0\n",
      "Stage 1 number of hits (true and false positives):  53.0\n",
      "Stage 1 TRUE positives:  50.0\n",
      "Stage 1 FALSE positives:  3.0\n",
      "Stage 1 TRUE negatives:  66.0\n",
      "Total number of flagged HPC-AD subjects:  39.0\n",
      "Number of flagged HPC-AD subjects that are TRUE positives:  39\n",
      "Number of flagged HPC-AD subjects that are FALSE positives:  0.0\n",
      "Number of true negatives:  69.0\n",
      "#############################\n",
      "Stage 1 stats for TARGET vs NON-TARGET\n",
      "Precision for AD:  0.9433962264150944\n",
      "Recall (or sensitivity)  for AD:  0.9090909090909091\n",
      "Specificity:  0.9565217391304348\n",
      "Adjusted precision for 33.6% baseline rate:  0.9136479712475173\n",
      "Accuracy:  0.9354838709677419\n",
      "#############################\n",
      "Stage 2 stats for TARGET vs NON-TARGET\n",
      "Precision for HPC-AD:  1.0\n",
      "Recall (or sensitivity) for HPC-AD:  0.7090909090909091\n",
      "Specificity:  1.0\n",
      "Adjusted precision for 33.6% baseline rate:  1.0\n",
      "Accuracy:  0.8709677419354839\n",
      "Stage 1\n",
      "Proba:\n",
      "[0.74103586 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         0.93023256 1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.96389892 0.76033058 0.99578059 0.99588477 1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.87854251 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         0.99606299 0.99603175 1.\n",
      " 1.         1.         1.         1.         1.         0.66791045\n",
      " 1.         0.38257576 0.9453125  0.58232932 0.96761134 1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         0.97154472 0.62256809 0.99601594\n",
      " 1.         1.         1.         0.33739837 1.         1.\n",
      " 1.         0.95634921 1.         1.         1.         1.\n",
      " 1.         0.68897638 0.59288538 1.         1.         0.75471698\n",
      " 0.196      0.99591837 0.81818182 1.         1.         1.\n",
      " 0.86111111 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         0.31698113\n",
      " 1.         0.69327731 1.         1.         1.         0.99586777\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         0.47244094 1.         1.         1.         0.87984496\n",
      " 1.         0.25910931 1.         1.         0.80519481 1.\n",
      " 1.         0.33603239 0.78039216 1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         0.99591837\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         0.51968504 1.         0.99591837\n",
      " 1.         1.         0.99206349 0.92720307 0.99610895 1.\n",
      " 1.         1.         0.99607843 1.         1.         1.\n",
      " 0.99579832 0.77366255 0.93951613 1.         1.         0.64435146\n",
      " 1.         1.         0.95275591 1.         1.         0.29707113\n",
      " 0.98823529 0.54077253 1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         0.9        1.         1.\n",
      " 1.         1.         1.         1.         1.         0.99585062\n",
      " 1.         1.         0.9626556  1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.99203187 1.         1.         1.         1.         1.\n",
      " 1.         0.972      1.         1.         0.97233202 0.9650655\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.98760331]\n",
      "Average hm score 0.7651821862348178\n",
      "Stage 2\n",
      "Adjusted gamma:  1.0\n",
      "Adjusted gamma:  1.0\n",
      "Classifying AD vs CN...\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 0.\n",
      " 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1.\n",
      " 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 0. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1.]\n",
      "Total number of TARGET subjects:  55.0\n",
      "Total number of NON-TARGET subjects:  68.0\n",
      "Stage 1 number of hits (true and false positives):  55.0\n",
      "Stage 1 TRUE positives:  51.0\n",
      "Stage 1 FALSE positives:  4.0\n",
      "Stage 1 TRUE negatives:  64.0\n",
      "Total number of flagged HPC-AD subjects:  42.0\n",
      "Number of flagged HPC-AD subjects that are TRUE positives:  41\n",
      "Number of flagged HPC-AD subjects that are FALSE positives:  1.0\n",
      "Number of true negatives:  67.0\n",
      "#############################\n",
      "Stage 1 stats for TARGET vs NON-TARGET\n",
      "Precision for AD:  0.9272727272727272\n",
      "Recall (or sensitivity)  for AD:  0.9272727272727272\n",
      "Specificity:  0.9411764705882353\n",
      "Adjusted precision for 33.6% baseline rate:  0.8886014788062178\n",
      "Accuracy:  0.9349593495934959\n",
      "#############################\n",
      "Stage 2 stats for TARGET vs NON-TARGET\n",
      "Precision for HPC-AD:  0.9761904761904762\n",
      "Recall (or sensitivity) for HPC-AD:  0.7454545454545455\n",
      "Specificity:  0.9852941176470589\n",
      "Adjusted precision for 33.6% baseline rate:  0.9624777044410289\n",
      "Accuracy:  0.8780487804878049\n",
      "Stage 1\n",
      "Proba:\n",
      "[0.53386454 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         0.95736434 1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.99277978 0.90082645 0.99578059 1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.96761134 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         0.96268657\n",
      " 1.         0.125      1.         0.34939759 1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         0.74319066 1.\n",
      " 1.         1.         1.         0.95934959 1.         1.\n",
      " 1.         0.96031746 1.         1.         1.         1.\n",
      " 1.         0.90944882 0.26086957 1.         1.         0.98113208\n",
      " 0.744      0.96326531 0.95041322 1.         1.         1.\n",
      " 0.86111111 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         0.00754717 1.         0.99159664 1.         1.\n",
      " 1.         1.         1.         1.         0.92647059 1.\n",
      " 1.         1.         0.99173554 1.         1.         0.12156863\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         0.96638655 1.         1.         1.\n",
      " 1.         1.         0.11417323 0.99183673 1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.9375     0.8362069  1.         1.         0.99607843 1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         0.99203187 1.\n",
      " 1.         1.         1.         1.         0.99183673 1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         0.99586777 1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         0.00806452 1.         1.         1.\n",
      " 0.70325203 1.         1.         0.84860558 0.71223022 1.\n",
      " 0.8625     0.98790323 0.78764479 0.99607843 1.         1.\n",
      " 0.88846154 1.         1.         0.         1.         1.\n",
      " 1.         1.         0.79166667 1.         1.         1.\n",
      " 1.        ]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average hm score 0.8218623481781376\n",
      "Stage 2\n",
      "Adjusted gamma:  1.0\n",
      "Adjusted gamma:  1.0\n",
      "Classifying AD vs CN...\n",
      "[0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 1.\n",
      " 1. 0. 0. 1. 0. 1. 0. 0. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1.\n",
      " 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 0.\n",
      " 0. 0. 1. 1. 0. 1. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1.]\n",
      "Total number of TARGET subjects:  55.0\n",
      "Total number of NON-TARGET subjects:  68.0\n",
      "Stage 1 number of hits (true and false positives):  56.0\n",
      "Stage 1 TRUE positives:  54.0\n",
      "Stage 1 FALSE positives:  2.0\n",
      "Stage 1 TRUE negatives:  66.0\n",
      "Total number of flagged HPC-AD subjects:  48.0\n",
      "Number of flagged HPC-AD subjects that are TRUE positives:  48\n",
      "Number of flagged HPC-AD subjects that are FALSE positives:  0.0\n",
      "Number of true negatives:  68.0\n",
      "#############################\n",
      "Stage 1 stats for TARGET vs NON-TARGET\n",
      "Precision for AD:  0.9642857142857143\n",
      "Recall (or sensitivity)  for AD:  0.9818181818181818\n",
      "Specificity:  0.9705882352941176\n",
      "Adjusted precision for 33.6% baseline rate:  0.9441091127245124\n",
      "Accuracy:  0.975609756097561\n",
      "#############################\n",
      "Stage 2 stats for TARGET vs NON-TARGET\n",
      "Precision for HPC-AD:  1.0\n",
      "Recall (or sensitivity) for HPC-AD:  0.8727272727272727\n",
      "Specificity:  1.0\n",
      "Adjusted precision for 33.6% baseline rate:  1.0\n",
      "Accuracy:  0.943089430894309\n"
     ]
    }
   ],
   "source": [
    "scores_ad_cn=[]\n",
    "scores_s2 = []\n",
    "ad_precision = []\n",
    "cn_precision = []\n",
    "ad_recall = []\n",
    "cn_recall = []\n",
    "ad_f1_score = []\n",
    "cn_f1_score = []\n",
    "\n",
    "s1_spec = []\n",
    "s1_sens = []\n",
    "s1_prec = []\n",
    "s1_acc = []\n",
    "\n",
    "s2_spec = []\n",
    "s2_sens = []\n",
    "s2_prec = []\n",
    "s2_acc = []\n",
    "\n",
    "\n",
    "skf = StratifiedKFold(n_splits=3)\n",
    "for train_index, val_index in skf.split(x_,y_):\n",
    "    X_training, X_val = x_[train_index], x_[val_index]\n",
    "    y_training, y_val = y_[train_index], y_[val_index]\n",
    "    \n",
    "    hpc = high_confidence_at.TwoStagesPrediction(\n",
    "        n_iter=500,\n",
    "        shuffle_test_split=0.5,\n",
    "        min_gamma=.99,\n",
    "        thresh_ratio=0.1)\n",
    "    \n",
    "    hpc.fit(X_training, X_training, y_training)\n",
    "    \n",
    "    _, dic_results = hpc.predict(X_val, X_val)\n",
    "    \n",
    "    # test in validation sample\n",
    "    acc = metrics.accuracy_score(y_val, (dic_results['s1df'][:,0]>0).astype(float))\n",
    "    tmp_mask = (dic_results['s2df'][:,1]>0)\n",
    "    acc_s2 = metrics.accuracy_score(y_val[tmp_mask], (dic_results['s1df'][:,0]>0).astype(float)[tmp_mask])\n",
    "    scores_ad_cn.append(acc)\n",
    "    scores_s2.append(acc_s2)\n",
    "    print('Classifying AD vs CN...')\n",
    "    print((dic_results['s1df'][:,0]>0).astype(float))\n",
    "    \n",
    "    y_pred = (dic_results['s1df'][:,0]>0).astype(float)\n",
    "    lr_decision = dic_results['s2df'][:,1]\n",
    "    \n",
    "    # BASE SVM PERFORMANCE\n",
    "    ad_p = metrics.precision_score(y_val, y_pred)\n",
    "    ad_precision.append(ad_p)\n",
    "    cn_p = metrics.precision_score(y_val, y_pred, pos_label=0)\n",
    "    cn_precision.append(cn_p)\n",
    "    ad_r = metrics.recall_score(y_val, y_pred)\n",
    "    ad_recall.append(ad_r)\n",
    "    cn_r = metrics.recall_score(y_val, y_pred, pos_label=0)\n",
    "    cn_recall.append(cn_r)\n",
    "    ad_f1 = metrics.f1_score(y_val, y_pred)\n",
    "    ad_f1_score.append(ad_f1)\n",
    "    cn_f1 = metrics.f1_score(y_val, y_pred, pos_label=0)\n",
    "    cn_f1_score.append(cn_f1)\n",
    "    \n",
    "    \n",
    "    sens, spec, prec, acc, sens_2, spec_2, prec_2, acc_2 = predic_stats(y_val, y_pred, lr_decision)\n",
    "    s1_spec.append(spec)\n",
    "    s1_sens.append(sens)\n",
    "    s1_prec.append(prec)\n",
    "    s1_acc.append(acc)\n",
    "    s2_spec.append(spec_2)\n",
    "    s2_sens.append(sens_2)\n",
    "    s2_prec.append(prec_2)\n",
    "    s2_acc.append(acc_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stage 1\n",
      "Mean sensitivity:  0.9393939393939394\n",
      "Mean specificity:  0.9560954816709293\n",
      "Mean precision:  0.944984889324512\n",
      "Mean accuracy:  0.9486843255529328\n",
      "##########\n",
      "Stage 2\n",
      "Mean sensitivity:  0.7757575757575758\n",
      "Mean specificity:  0.9950980392156863\n",
      "Mean precision:  0.9920634920634921\n",
      "Mean accuracy:  0.8973686511058659\n"
     ]
    }
   ],
   "source": [
    "print('Stage 1')\n",
    "print('Mean sensitivity: ', np.mean(s1_sens))\n",
    "print('Mean specificity: ', np.mean(s1_spec))\n",
    "print('Mean precision: ', np.mean(s1_prec))\n",
    "print('Mean accuracy: ', np.mean(s1_acc))\n",
    "print('#'*10)\n",
    "print('Stage 2')\n",
    "print('Mean sensitivity: ', np.mean(s2_sens))\n",
    "print('Mean specificity: ', np.mean(s2_spec))\n",
    "print('Mean precision: ', np.mean(s2_prec))\n",
    "print('Mean accuracy: ', np.mean(s2_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train HPC (on whole training set of ADNI1 AD & CN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stage 1\n",
      "Proba:\n",
      "[0.77083333 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         0.96062992 1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         0.90151515 1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.9757085  1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         0.98360656\n",
      " 1.         0.33198381 1.         0.38       1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         0.69196429 1.\n",
      " 1.         1.         1.         0.875      1.         1.\n",
      " 1.         0.99242424 1.         1.         1.         1.\n",
      " 1.         0.94142259 0.53815261 1.         1.         0.99610895\n",
      " 0.36144578 1.         1.         1.         1.         1.\n",
      " 0.94979079 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         0.         1.         0.97628458 1.         1.\n",
      " 1.         1.         1.         1.         0.94605809 1.\n",
      " 1.         1.         1.         1.         1.         0.46840149\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         0.18518519 0.98455598 1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         0.99183673 1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         0.99607843 1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         0.46413502 1.         1.\n",
      " 0.         1.         1.         1.         1.         1.\n",
      " 0.83984375 1.         1.         1.         1.         0.87124464\n",
      " 0.61570248 1.         0.37398374 1.         0.80232558 0.96707819\n",
      " 0.99579832 1.         1.         1.         1.         1.\n",
      " 0.49361702 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.87747036 1.         1.         1.         1.         1.\n",
      " 0.         1.         1.         0.99588477 1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         0.98449612 1.         1.         0.73617021 1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         0.96694215 1.\n",
      " 1.         1.         0.17716535 1.         1.         1.\n",
      " 1.         1.         0.99555556 1.         0.98007968 1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 0.99593496 1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         0.97254902\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         0.98818898 0.92622951 1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.        ]\n",
      "Average hm score 0.8756756756756757\n",
      "Stage 2\n",
      "Adjusted gamma:  1.0\n",
      "Adjusted gamma:  1.0\n"
     ]
    }
   ],
   "source": [
    "#reload(high_confidence)\n",
    "hpc = high_confidence_at.TwoStagesPrediction(\n",
    "    n_iter=500,\n",
    "    shuffle_test_split=0.5,\n",
    "    min_gamma=.99,\n",
    "    thresh_ratio=0.1)\n",
    "\n",
    "hpc.fit(x_, x_, y_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Level 1\n",
      "------------------------\n",
      "Ratio: 1.0\n",
      "#    :  165.0\n",
      "# true values:  370\n",
      "ACC  :  0.9783783783783784\n",
      "Level 2\n",
      "------------------------\n",
      "Ratio: 0.8242424242424242\n",
      "#    :  136.0\n",
      "# true values:  136\n",
      "ACC  :  1.0\n"
     ]
    }
   ],
   "source": [
    "array_results, dic_results = hpc.predict(x_, x_)\n",
    "\n",
    "# Level 1\n",
    "print('Level 1')\n",
    "stats_mask(y_, (dic_results['s1df'][:,0]>0).astype(float))\n",
    "\n",
    "print('Level 2')\n",
    "stats_mask(y_, (dic_results['s1df'][:,0]>0).astype(float), dic_results['s2df'][:,1]>0)\n",
    "#stats_mask(dic_results['s2df'][:,2]>0)\n",
    "#stats_mask(dic_results['s2df'][:,3]>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = (dic_results['s1df'][:,0]>0).astype(float)\n",
    "lr_decision = dic_results['s2df'][:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stage 1 stats for AD vs CN\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.97      0.99      0.98       205\n",
      "        1.0       0.99      0.96      0.98       165\n",
      "\n",
      "avg / total       0.98      0.98      0.98       370\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Stage 1 stats for AD vs CN')\n",
    "print(metrics.classification_report(y_, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of TARGET subjects:  165.0\n",
      "Total number of NON-TARGET subjects:  205.0\n",
      "Stage 1 number of hits (true and false positives):  161.0\n",
      "Stage 1 TRUE positives:  159.0\n",
      "Stage 1 FALSE positives:  2.0\n",
      "Stage 1 TRUE negatives:  203.0\n",
      "Total number of flagged HPC-AD subjects:  136.0\n",
      "Number of flagged HPC-AD subjects that are TRUE positives:  136\n",
      "Number of flagged HPC-AD subjects that are FALSE positives:  0.0\n",
      "Number of true negatives:  205.0\n",
      "#############################\n",
      "Stage 1 stats for TARGET vs NON-TARGET\n",
      "Precision for AD:  0.9875776397515528\n",
      "Recall (or sensitivity)  for AD:  0.9636363636363636\n",
      "Specificity:  0.9902439024390244\n",
      "Adjusted precision for 33.6% baseline rate:  0.980384995488334\n",
      "Accuracy:  0.9783783783783784\n",
      "#############################\n",
      "Stage 2 stats for TARGET vs NON-TARGET\n",
      "Precision for HPC-AD:  1.0\n",
      "Recall (or sensitivity) for HPC-AD:  0.8242424242424242\n",
      "Specificity:  1.0\n",
      "Adjusted precision for 33.6% baseline rate:  1.0\n",
      "Accuracy:  0.9216216216216216\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9636363636363636,\n",
       " 0.9902439024390244,\n",
       " 0.9875776397515528,\n",
       " 0.9783783783783784,\n",
       " 0.8242424242424242,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.9216216216216216)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predic_stats(y_, y_pred, lr_decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# compare with other methods "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(370,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base = high_confidence_at.BaseSvc()\n",
    "base.fit(x_, y_)\n",
    "y_predicted = base.predict(x_)\n",
    "y_score = base.decision_function(x_)\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_b = dict()\n",
    "tpr_b = dict()\n",
    "roc_auc_b = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_b[i], tpr_b[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_b[i] = auc(fpr_b[i], tpr_b[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_b[\"micro\"], tpr_b[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_b[\"micro\"] = auc(fpr_b[\"micro\"], tpr_b[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 1.00\n"
     ]
    }
   ],
   "source": [
    "average_precision_b = average_precision_score(y_true, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision_b))\n",
    "\n",
    "precision_b, recall_b, _ = precision_recall_curve(y_true, y_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(lr_decision, (y_score.shape[0],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_h = dict()\n",
    "tpr_h = dict()\n",
    "roc_auc_h = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_h[i], tpr_h[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_h[i] = auc(fpr_h[i], tpr_h[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_h[\"micro\"], tpr_h[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_h[\"micro\"] = auc(fpr_h[\"micro\"], tpr_h[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 1.00\n"
     ]
    }
   ],
   "source": [
    "average_precision_h = average_precision_score(y_true, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision_h))\n",
    "\n",
    "precision_h, recall_h, _ = precision_recall_curve(y_true, y_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rbf kernel svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(370,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_param_grid=dict(C=(np.logspace(-2, 1, 15)))\n",
    "\n",
    "clf_svm = SVC(kernel='rbf', class_weight='balanced', decision_function_shape='ovr', random_state=1)\n",
    "grclf_svm = GridSearchCV(clf_svm, param_grid=svm_param_grid, \n",
    "                         cv=StratifiedShuffleSplit(n_splits=50, test_size=.2, random_state=1))               \n",
    "\n",
    "grclf_svm.fit(x_, y_)\n",
    "y_predicted = grclf_svm.predict(x_)\n",
    "y_score = grclf_svm.decision_function(x_)\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_svcrbf = dict()\n",
    "tpr_svcrbf = dict()\n",
    "roc_auc_svcrbf = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_svcrbf[i], tpr_svcrbf[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_svcrbf[i] = auc(fpr_svcrbf[i], tpr_svcrbf[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_svcrbf[\"micro\"], tpr_svcrbf[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_svcrbf[\"micro\"] = auc(fpr_svcrbf[\"micro\"], tpr_svcrbf[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k nearest neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(370,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_range = list(range(3,7))\n",
    "weight_opt = [\"uniform\", \"distance\"]\n",
    "knn_param_grid = dict(n_neighbors = k_range, weights = weight_opt)\n",
    "\n",
    "clf_knn = KNeighborsClassifier(algorithm='auto')\n",
    "grclf_knn = GridSearchCV(clf_knn, param_grid=knn_param_grid, \n",
    "                         cv=StratifiedShuffleSplit(n_splits=50, test_size=.2, random_state=1))\n",
    "grclf_knn.fit(x_, y_)\n",
    "y_predicted = grclf_knn.predict(x_)\n",
    "#y_score = clf.decision_function(x_)\n",
    "y_score = grclf_knn.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_knn = dict()\n",
    "tpr_knn = dict()\n",
    "roc_auc_knn = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_knn[i], tpr_knn[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_knn[i] = auc(fpr_knn[i], tpr_knn[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_knn[\"micro\"], tpr_knn[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_knn[\"micro\"] = auc(fpr_knn[\"micro\"], tpr_knn[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_features = x_.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(370,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_param_grid = [\n",
    "{'n_estimators': [10, 25], 'max_features': [5, n_features], \n",
    " 'max_depth': [10, 50, None], 'bootstrap': [True, False]}\n",
    "]\n",
    "\n",
    "clf_rf = RandomForestClassifier(random_state=1)\n",
    "grclf_rf = GridSearchCV(clf_rf, param_grid=rf_param_grid, \n",
    "                       cv=StratifiedShuffleSplit(n_splits=50, test_size=.2, random_state=1))\n",
    "grclf_rf.fit(x_, y_)\n",
    "y_predicted = grclf_rf.predict(x_)\n",
    "#y_score = clf.decision_function(x_)\n",
    "y_score = grclf_rf.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_rf = dict()\n",
    "tpr_rf = dict()\n",
    "roc_auc_rf = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_rf[i], tpr_rf[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_rf[i] = auc(fpr_rf[i], tpr_rf[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_rf[\"micro\"], tpr_rf[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_rf[\"micro\"] = auc(fpr_rf[\"micro\"], tpr_rf[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gaussian naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(370,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_gnb = GaussianNB()\n",
    "clf_gnb.fit(x_, y_)\n",
    "y_predicted = clf_gnb.predict(x_)\n",
    "#y_score = clf.decision_function(x_)\n",
    "y_score = clf_gnb.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_nb = dict()\n",
    "tpr_nb = dict()\n",
    "roc_auc_nb = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_nb[i], tpr_nb[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_nb[i] = auc(fpr_nb[i], tpr_nb[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_nb[\"micro\"], tpr_nb[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_nb[\"micro\"] = auc(fpr_nb[\"micro\"], tpr_nb[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure()\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(10,8)\n",
    "lw = 4\n",
    "plt.rc('xtick', labelsize=40)\n",
    "plt.rc('ytick', labelsize=40)\n",
    "plt.plot(fpr_svcrbf[0], tpr_svcrbf[0], color='green',\n",
    "         lw=lw, label='RBF SVM (AUC=%0.3f)' % roc_auc_svcrbf[0])\n",
    "plt.plot(fpr_knn[0], tpr_knn[0], color='pink',\n",
    "         lw=lw, label='KNN (AUC=%0.3f)' % roc_auc_knn[0])\n",
    "plt.plot(fpr_rf[0], tpr_rf[0], color='brown',\n",
    "         lw=lw, label='RF (AUC=%0.3f)' % roc_auc_rf[0])\n",
    "plt.plot(fpr_nb[0], tpr_nb[0], color='orange',\n",
    "         lw=lw, label='GNB (AUC=%0.3f)' % roc_auc_nb[0])\n",
    "plt.plot(fpr_b[0], tpr_b[0], color='blue',\n",
    "         lw=lw, label='Base (AUC=%0.3f)' % roc_auc_b[0])\n",
    "plt.plot(fpr_h[0], tpr_h[0], color='red',\n",
    "         lw=lw, label='HPS (AUC=%0.3f)' % roc_auc_h[0])\n",
    "plt.plot([0, 1], [0, 1], color='grey', lw=lw, linestyle='--')\n",
    "plt.xlim([-0.05, 1.00])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('FPR', fontdict={'size': 40})\n",
    "plt.ylabel('TPR', fontdict={'size': 40})\n",
    "plt.title('ADNI1 AD vs CN', fontdict={'size': 40})\n",
    "plt.legend(loc=\"lower right\", prop={'size': 25})\n",
    "plt.show()\n",
    "fig.savefig(path_results + 'adni1_ad_roc_multi.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test on ADNI1 MCI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the data\n",
    "adni1_mci = pd.read_csv('/home/angela/Desktop/vcog_paper/adni1_vbm_adcn_subtypes_20171209/7clus/adni1_mci_bl_demog_weights.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_mci = adni1_mci.loc[:,'MCI'].values.astype(bool)\n",
    "adni1_mci = adni1_mci.iloc[mask_mci]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "adni1_mci.dropna(subset=['sub1','age_scan','gender','mean_gm','tiv',\n",
    "                         'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "adni1_mci = adni1_mci[['RID','age_scan','gender','mean_gm','tiv',\n",
    "             'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR',\n",
    "             'sub1','sub2','sub3','sub4','sub5','sub6','sub7',\n",
    "             'ABETA','TAU','conv_2_ad','AD','MCI','CN','APOE4_bin','DX','Month_conv']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(adni1_mci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((235, 16), (235,), (235, 4))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_ = adni1_mci.iloc[:,adni1_mci.columns.get_loc(\"ADAS13\"):adni1_mci.columns.get_loc(\"sub7\")+1].values\n",
    "#x_ = scalerX.transform(x_)\n",
    "y_ = adni1_mci['conv_2_ad'].values.ravel()\n",
    "\n",
    "\n",
    "confounds = adni1_mci[['gender','age_scan','mean_gm','tiv']].values\n",
    "#confounds = data[['sex','age_r']].values[mask_mci,:]\n",
    "#confounds[:, 1:] = scaler.transform(confounds[:, 1:])\n",
    "#confounds[:, 0] = preprocessing.binarize(confounds[:, 0].reshape(-1, 1), threshold=1)[:, 0]\n",
    "#confounds = scaler.transform(confounds)\n",
    "#x_ = crm.transform(confounds, x_)\n",
    "\n",
    "x_ = scaler.transform(np.hstack((x_,confounds)))\n",
    "\n",
    "x_.shape, y_.shape, confounds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Level 1\n",
      "------------------------\n",
      "Ratio: 1.0\n",
      "#    :  147.0\n",
      "# true values:  235\n",
      "ACC  :  0.7702127659574468\n",
      "Level 2\n",
      "------------------------\n",
      "Ratio: 0.5510204081632653\n",
      "#    :  81.0\n",
      "# true values:  87\n",
      "ACC  :  0.9310344827586207\n"
     ]
    }
   ],
   "source": [
    "array_results, dic_results = hpc.predict(x_, x_)\n",
    "\n",
    "# Level 1\n",
    "print('Level 1')\n",
    "stats_mask(y_, (dic_results['s1df'][:,0]>0).astype(float))\n",
    "\n",
    "print('Level 2')\n",
    "stats_mask(y_, (dic_results['s1df'][:,0]>0).astype(float), dic_results['s2df'][:,1]>0)\n",
    "#stats_mask(dic_results['s2df'][:,2]>0)\n",
    "#stats_mask(dic_results['s2df'][:,3]>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = (dic_results['s1df'][:,0]>0).astype(float)\n",
    "lr_decision = dic_results['s2df'][:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of TARGET subjects:  147.0\n",
      "Total number of NON-TARGET subjects:  88.0\n",
      "Stage 1 number of hits (true and false positives):  137.0\n",
      "Stage 1 TRUE positives:  115.0\n",
      "Stage 1 FALSE positives:  22.0\n",
      "Stage 1 TRUE negatives:  66.0\n",
      "Total number of flagged HPC-AD subjects:  87.0\n",
      "Number of flagged HPC-AD subjects that are TRUE positives:  81\n",
      "Number of flagged HPC-AD subjects that are FALSE positives:  6.0\n",
      "Number of true negatives:  82.0\n",
      "#############################\n",
      "Stage 1 stats for TARGET vs NON-TARGET\n",
      "Precision for AD:  0.8394160583941606\n",
      "Recall (or sensitivity)  for AD:  0.782312925170068\n",
      "Specificity:  0.75\n",
      "Adjusted precision for 33.6% baseline rate:  0.6129247168554297\n",
      "Accuracy:  0.7702127659574468\n",
      "#############################\n",
      "Stage 2 stats for TARGET vs NON-TARGET\n",
      "Precision for HPC-AD:  0.9310344827586207\n",
      "Recall (or sensitivity) for HPC-AD:  0.5510204081632653\n",
      "Specificity:  0.9318181818181818\n",
      "Adjusted precision for 33.6% baseline rate:  0.8035170781197157\n",
      "Accuracy:  0.6936170212765957\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.782312925170068,\n",
       " 0.75,\n",
       " 0.8394160583941606,\n",
       " 0.7702127659574468,\n",
       " 0.5510204081632653,\n",
       " 0.9318181818181818,\n",
       " 0.9310344827586207,\n",
       " 0.6936170212765957)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predic_stats(y_, y_pred, lr_decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC curve "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#base = high_confidence_at.BaseSvc()\n",
    "#base.fit(x_, y_)\n",
    "y_predicted = base.predict(x_)\n",
    "y_score = base.decision_function(x_)\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_b = dict()\n",
    "tpr_b = dict()\n",
    "roc_auc_b = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_b[i], tpr_b[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_b[i] = auc(fpr_b[i], tpr_b[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_b[\"micro\"], tpr_b[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_b[\"micro\"] = auc(fpr_b[\"micro\"], tpr_b[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.91\n"
     ]
    }
   ],
   "source": [
    "average_precision_b = average_precision_score(y_true, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision_b))\n",
    "\n",
    "precision_b, recall_b, _ = precision_recall_curve(y_true, y_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HPS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(lr_decision, (y_score.shape[0],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_h = dict()\n",
    "tpr_h = dict()\n",
    "roc_auc_h = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_h[i], tpr_h[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_h[i] = auc(fpr_h[i], tpr_h[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_h[\"micro\"], tpr_h[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_h[\"micro\"] = auc(fpr_h[\"micro\"], tpr_h[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rbf kernel svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = grclf_svm.predict(x_)\n",
    "y_score = grclf_svm.decision_function(x_)\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_svcrbf = dict()\n",
    "tpr_svcrbf = dict()\n",
    "roc_auc_svcrbf = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_svcrbf[i], tpr_svcrbf[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_svcrbf[i] = auc(fpr_svcrbf[i], tpr_svcrbf[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_svcrbf[\"micro\"], tpr_svcrbf[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_svcrbf[\"micro\"] = auc(fpr_svcrbf[\"micro\"], tpr_svcrbf[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k nearest neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = grclf_knn.predict(x_)\n",
    "y_score = grclf_knn.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_knn = dict()\n",
    "tpr_knn = dict()\n",
    "roc_auc_knn = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_knn[i], tpr_knn[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_knn[i] = auc(fpr_knn[i], tpr_knn[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_knn[\"micro\"], tpr_knn[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_knn[\"micro\"] = auc(fpr_knn[\"micro\"], tpr_knn[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = grclf_rf.predict(x_)\n",
    "y_score = grclf_rf.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_rf = dict()\n",
    "tpr_rf = dict()\n",
    "roc_auc_rf = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_rf[i], tpr_rf[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_rf[i] = auc(fpr_rf[i], tpr_rf[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_rf[\"micro\"], tpr_rf[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_rf[\"micro\"] = auc(fpr_rf[\"micro\"], tpr_rf[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gaussian naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = clf_gnb.predict(x_)\n",
    "y_score = clf_gnb.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_nb = dict()\n",
    "tpr_nb = dict()\n",
    "roc_auc_nb = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_nb[i], tpr_nb[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_nb[i] = auc(fpr_nb[i], tpr_nb[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_nb[\"micro\"], tpr_nb[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_nb[\"micro\"] = auc(fpr_nb[\"micro\"], tpr_nb[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure()\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(10,8)\n",
    "lw = 4\n",
    "plt.rc('xtick', labelsize=40)\n",
    "plt.rc('ytick', labelsize=40)\n",
    "plt.plot(fpr_svcrbf[0], tpr_svcrbf[0], color='green',\n",
    "         lw=lw, label='RBF SVM (AUC=%0.3f)' % roc_auc_svcrbf[0])\n",
    "plt.plot(fpr_knn[0], tpr_knn[0], color='pink',\n",
    "         lw=lw, label='KNN (AUC=%0.3f)' % roc_auc_knn[0])\n",
    "plt.plot(fpr_rf[0], tpr_rf[0], color='brown',\n",
    "         lw=lw, label='RF (AUC=%0.3f)' % roc_auc_rf[0])\n",
    "plt.plot(fpr_nb[0], tpr_nb[0], color='orange',\n",
    "         lw=lw, label='GNB (AUC=%0.3f)' % roc_auc_nb[0])\n",
    "plt.plot(fpr_b[0], tpr_b[0], color='blue',\n",
    "         lw=lw, label='Base (AUC=%0.3f)' % roc_auc_b[0])\n",
    "plt.plot(fpr_h[0], tpr_h[0], color='red',\n",
    "         lw=lw, label='HPS (AUC=%0.3f)' % roc_auc_h[0])\n",
    "plt.plot([0, 1], [0, 1], color='grey', lw=lw, linestyle='--')\n",
    "plt.xlim([-0.05, 1.00])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('FPR', fontdict={'size': 40})\n",
    "plt.ylabel('TPR', fontdict={'size': 40})\n",
    "plt.title('ADNI1 pMCI vs sMCI', fontdict={'size': 40})\n",
    "plt.legend(loc=\"lower right\", prop={'size': 25})\n",
    "#ax.legend(loc='center left', bbox_to_anchor=(1,0.5), prop={'size': 30})\n",
    "plt.show()\n",
    "fig.savefig(path_results + 'adni1_mci_roc_multi.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REPLICATION IN ADNI2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "adni2_df = pd.read_csv('/home/angela/Desktop/vcog_paper/adni1_vbm_adcn_subtypes_20171209/7clus/adni2_model_weights.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "adni2_df.drop(adni2_df[adni2_df.RID < 2000].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get rid of NaNs\n",
    "adni2_df.dropna(axis=0,how='any',subset=['sub1','gender','age_scan','mean_gm','tiv','conv_2_ad',\n",
    "                                        'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "adni2_df = adni2_df[['RID','age_scan','gender','mean_gm','tiv',\n",
    "             'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR',\n",
    "             'sub1','sub2','sub3','sub4','sub5','sub6','sub7',\n",
    "             'ABETA','TAU','conv_2_ad','AD','MCI','CN','APOE4_bin','DX']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AD vs CN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((276, 16), (276,), (276, 4))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mask of the AD and CN subjects only\n",
    "mask_cnad = adni2_df.loc[:,['CN','AD']].values.sum(1).astype(bool)\n",
    "\n",
    "x_ = adni2_df.iloc[mask_cnad,adni2_df.columns.get_loc(\"ADAS13\"):adni2_df.columns.get_loc(\"sub7\")+1].values\n",
    "#x_ = scalerX.transform(x_)\n",
    "y_ = adni2_df[['AD']].values.ravel()[mask_cnad]\n",
    "\n",
    "\n",
    "confounds = adni2_df[['gender','age_scan','mean_gm','tiv']].values[mask_cnad,:]\n",
    "#confounds = data[['sex','age_r']].values[mask_mci,:]\n",
    "#confounds[:, 1:] = scaler.transform(confounds[:, 1:])\n",
    "#confounds[:, 0] = preprocessing.binarize(confounds[:, 0].reshape(-1, 1), threshold=1)[:, 0]\n",
    "#confounds = scaler.transform(confounds)\n",
    "#x_ = crm.transform(confounds, x_)\n",
    "\n",
    "x_ = scaler.transform(np.hstack((x_,confounds)))\n",
    "\n",
    "x_.shape, y_.shape, confounds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Level 1\n",
      "------------------------\n",
      "Ratio: 1.0\n",
      "#    :  88.0\n",
      "# true values:  276\n",
      "ACC  :  0.9492753623188406\n",
      "Level 2\n",
      "------------------------\n",
      "Ratio: 0.7954545454545454\n",
      "#    :  70.0\n",
      "# true values:  71\n",
      "ACC  :  0.9859154929577465\n"
     ]
    }
   ],
   "source": [
    "array_results, dic_results = hpc.predict(x_, x_)\n",
    "\n",
    "# Level 1\n",
    "print('Level 1')\n",
    "stats_mask(y_, (dic_results['s1df'][:,0]>0).astype(float))\n",
    "\n",
    "print('Level 2')\n",
    "stats_mask(y_, (dic_results['s1df'][:,0]>0).astype(float), dic_results['s2df'][:,1]>0)\n",
    "#stats_mask(dic_results['s2df'][:,2]>0)\n",
    "#stats_mask(dic_results['s2df'][:,3]>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = (dic_results['s1df'][:,0]>0).astype(float)\n",
    "lr_decision = dic_results['s2df'][:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of TARGET subjects:  88.0\n",
      "Total number of NON-TARGET subjects:  188.0\n",
      "Stage 1 number of hits (true and false positives):  88.0\n",
      "Stage 1 TRUE positives:  81.0\n",
      "Stage 1 FALSE positives:  7.0\n",
      "Stage 1 TRUE negatives:  181.0\n",
      "Total number of flagged HPC-AD subjects:  71.0\n",
      "Number of flagged HPC-AD subjects that are TRUE positives:  70\n",
      "Number of flagged HPC-AD subjects that are FALSE positives:  1.0\n",
      "Number of true negatives:  187.0\n",
      "#############################\n",
      "Stage 1 stats for TARGET vs NON-TARGET\n",
      "Precision for AD:  0.9204545454545454\n",
      "Recall (or sensitivity)  for AD:  0.9204545454545454\n",
      "Specificity:  0.9627659574468085\n",
      "Adjusted precision for 33.6% baseline rate:  0.9259769742176098\n",
      "Accuracy:  0.9492753623188406\n",
      "#############################\n",
      "Stage 2 stats for TARGET vs NON-TARGET\n",
      "Precision for HPC-AD:  0.9859154929577465\n",
      "Recall (or sensitivity) for HPC-AD:  0.7954545454545454\n",
      "Specificity:  0.9946808510638298\n",
      "Adjusted precision for 33.6% baseline rate:  0.9869577018127793\n",
      "Accuracy:  0.9311594202898551\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9204545454545454,\n",
       " 0.9627659574468085,\n",
       " 0.9204545454545454,\n",
       " 0.9492753623188406,\n",
       " 0.7954545454545454,\n",
       " 0.9946808510638298,\n",
       " 0.9859154929577465,\n",
       " 0.9311594202898551)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predic_stats(y_, y_pred, lr_decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276,)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = base.predict(x_)\n",
    "y_score = base.decision_function(x_)\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_b = dict()\n",
    "tpr_b = dict()\n",
    "roc_auc_b = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_b[i], tpr_b[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_b[i] = auc(fpr_b[i], tpr_b[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_b[\"micro\"], tpr_b[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_b[\"micro\"] = auc(fpr_b[\"micro\"], tpr_b[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.98\n"
     ]
    }
   ],
   "source": [
    "average_precision_b = average_precision_score(y_true, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision_b))\n",
    "\n",
    "precision_b, recall_b, _ = precision_recall_curve(y_true, y_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HPS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(lr_decision, (y_score.shape[0],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_h = dict()\n",
    "tpr_h = dict()\n",
    "roc_auc_h = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_h[i], tpr_h[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_h[i] = auc(fpr_h[i], tpr_h[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_h[\"micro\"], tpr_h[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_h[\"micro\"] = auc(fpr_h[\"micro\"], tpr_h[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.98\n"
     ]
    }
   ],
   "source": [
    "average_precision_h = average_precision_score(y_true, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision_h))\n",
    "\n",
    "precision_h, recall_h, _ = precision_recall_curve(y_true, y_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rbf kernel svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276,)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = grclf_svm.predict(x_)\n",
    "y_score = grclf_svm.decision_function(x_)\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_svcrbf = dict()\n",
    "tpr_svcrbf = dict()\n",
    "roc_auc_svcrbf = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_svcrbf[i], tpr_svcrbf[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_svcrbf[i] = auc(fpr_svcrbf[i], tpr_svcrbf[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_svcrbf[\"micro\"], tpr_svcrbf[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_svcrbf[\"micro\"] = auc(fpr_svcrbf[\"micro\"], tpr_svcrbf[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k nearest neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276,)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = grclf_knn.predict(x_)\n",
    "y_score = grclf_knn.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_knn = dict()\n",
    "tpr_knn = dict()\n",
    "roc_auc_knn = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_knn[i], tpr_knn[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_knn[i] = auc(fpr_knn[i], tpr_knn[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_knn[\"micro\"], tpr_knn[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_knn[\"micro\"] = auc(fpr_knn[\"micro\"], tpr_knn[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276,)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = grclf_rf.predict(x_)\n",
    "y_score = grclf_rf.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_rf = dict()\n",
    "tpr_rf = dict()\n",
    "roc_auc_rf = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_rf[i], tpr_rf[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_rf[i] = auc(fpr_rf[i], tpr_rf[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_rf[\"micro\"], tpr_rf[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_rf[\"micro\"] = auc(fpr_rf[\"micro\"], tpr_rf[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gaussian naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276,)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = clf_gnb.predict(x_)\n",
    "y_score = clf_gnb.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_nb = dict()\n",
    "tpr_nb = dict()\n",
    "roc_auc_nb = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_nb[i], tpr_nb[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_nb[i] = auc(fpr_nb[i], tpr_nb[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_nb[\"micro\"], tpr_nb[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_nb[\"micro\"] = auc(fpr_nb[\"micro\"], tpr_nb[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure()\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(10,8)\n",
    "lw = 4\n",
    "plt.rc('xtick', labelsize=40)\n",
    "plt.rc('ytick', labelsize=40)\n",
    "plt.plot(fpr_svcrbf[0], tpr_svcrbf[0], color='green',\n",
    "         lw=lw, label='RBF SVM (AUC=%0.3f)' % roc_auc_svcrbf[0])\n",
    "plt.plot(fpr_knn[0], tpr_knn[0], color='pink',\n",
    "         lw=lw, label='KNN (AUC=%0.3f)' % roc_auc_knn[0])\n",
    "plt.plot(fpr_rf[0], tpr_rf[0], color='brown',\n",
    "         lw=lw, label='RF (AUC=%0.3f)' % roc_auc_rf[0])\n",
    "plt.plot(fpr_nb[0], tpr_nb[0], color='orange',\n",
    "         lw=lw, label='GNB (AUC=%0.3f)' % roc_auc_nb[0])\n",
    "plt.plot(fpr_b[0], tpr_b[0], color='blue',\n",
    "         lw=lw, label='Base (AUC=%0.3f)' % roc_auc_b[0])\n",
    "plt.plot(fpr_h[0], tpr_h[0], color='red',\n",
    "         lw=lw, label='HPS (AUC=%0.3f)' % roc_auc_h[0])\n",
    "plt.plot([0, 1], [0, 1], color='grey', lw=lw, linestyle='--')\n",
    "plt.xlim([-0.05, 1.00])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('FPR', fontdict={'size': 40})\n",
    "plt.ylabel('TPR', fontdict={'size': 40})\n",
    "plt.title('ADNI2 AD vs CN', fontdict={'size': 40})\n",
    "plt.legend(loc=\"lower right\", prop={'size': 25})\n",
    "#ax.legend(loc='center left', bbox_to_anchor=(1,0.5), prop={'size': 30})\n",
    "plt.show()\n",
    "fig.savefig(path_results + 'adni2_ad_roc_multi.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MCI stable vs converters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "adni2_mci = pd.read_csv('/home/angela/Desktop/vcog_paper/adni1_vbm_adcn_subtypes_20171209/7clus/adni2_mci_bl_demog_weights.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_mci = adni2_mci.loc[:,'MCI'].values.astype(bool)\n",
    "adni2_mci = adni2_mci.iloc[mask_mci]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "adni2_mci.dropna(subset=['sub1','age_scan','gender','mean_gm','tiv',\n",
    "                        'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "adni2_mci = adni2_mci[['RID','age_scan','gender','mean_gm','tiv',\n",
    "             'ADAS13','ADNI_MEM','ADNI_EF','BNTTOTAL','CLOCKSCOR',\n",
    "             'sub1','sub2','sub3','sub4','sub5','sub6','sub7',\n",
    "             'ABETA','TAU','conv_2_ad','AD','MCI','CN','APOE4_bin','DX','SUMMARYSUVR_WHOLECEREBNORM_1.11CUTOFF',\n",
    "                       'Month_conv']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((235, 16), (235,), (235, 4))"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_ = adni2_mci.iloc[:, adni2_mci.columns.get_loc(\"ADAS13\"):adni2_mci.columns.get_loc(\"sub7\")+1].values\n",
    "#x_ = scalerX.transform(x_)\n",
    "y_ = adni2_mci[['conv_2_ad']].values.ravel()\n",
    "\n",
    "\n",
    "confounds = adni2_mci[['gender','age_scan','mean_gm','tiv']].values\n",
    "#confounds = data[['sex','age_r']].values[mask_mci,:]\n",
    "#confounds[:, 1:] = scaler.transform(confounds[:, 1:])\n",
    "#confounds[:, 0] = preprocessing.binarize(confounds[:, 0].reshape(-1, 1), threshold=1)[:, 0]\n",
    "#confounds = scaler.transform(confounds)\n",
    "#x_ = crm.transform(confounds, x_)\n",
    "\n",
    "x_ = scaler.transform(np.hstack((x_,confounds)))\n",
    "\n",
    "x_.shape, y_.shape, confounds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Level 1\n",
      "------------------------\n",
      "Ratio: 1.0\n",
      "#    :  55.0\n",
      "# true values:  235\n",
      "ACC  :  0.8553191489361702\n",
      "Level 2\n",
      "------------------------\n",
      "Ratio: 0.4727272727272727\n",
      "#    :  26.0\n",
      "# true values:  32\n",
      "ACC  :  0.8125\n"
     ]
    }
   ],
   "source": [
    "array_results, dic_results = hpc.predict(x_, x_)\n",
    "\n",
    "# Level 1\n",
    "print('Level 1')\n",
    "stats_mask(y_, (dic_results['s1df'][:,0]>0).astype(float))\n",
    "\n",
    "print('Level 2')\n",
    "stats_mask(y_, (dic_results['s1df'][:,0]>0).astype(float), dic_results['s2df'][:,1]>0)\n",
    "#stats_mask(dic_results['s2df'][:,2]>0)\n",
    "#stats_mask(dic_results['s2df'][:,3]>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = (dic_results['s1df'][:,0]>0).astype(float)\n",
    "lr_decision = dic_results['s2df'][:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of TARGET subjects:  55.0\n",
      "Total number of NON-TARGET subjects:  180.0\n",
      "Stage 1 number of hits (true and false positives):  59.0\n",
      "Stage 1 TRUE positives:  40.0\n",
      "Stage 1 FALSE positives:  19.0\n",
      "Stage 1 TRUE negatives:  161.0\n",
      "Total number of flagged HPC-AD subjects:  32.0\n",
      "Number of flagged HPC-AD subjects that are TRUE positives:  26\n",
      "Number of flagged HPC-AD subjects that are FALSE positives:  6.0\n",
      "Number of true negatives:  174.0\n",
      "#############################\n",
      "Stage 1 stats for TARGET vs NON-TARGET\n",
      "Precision for AD:  0.6779661016949152\n",
      "Recall (or sensitivity)  for AD:  0.7272727272727273\n",
      "Specificity:  0.8944444444444445\n",
      "Adjusted precision for 33.6% baseline rate:  0.7771082015238928\n",
      "Accuracy:  0.8553191489361702\n",
      "#############################\n",
      "Stage 2 stats for TARGET vs NON-TARGET\n",
      "Precision for HPC-AD:  0.8125\n",
      "Recall (or sensitivity) for HPC-AD:  0.4727272727272727\n",
      "Specificity:  0.9666666666666667\n",
      "Adjusted precision for 33.6% baseline rate:  0.8776959142665774\n",
      "Accuracy:  0.851063829787234\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.7272727272727273,\n",
       " 0.8944444444444445,\n",
       " 0.6779661016949152,\n",
       " 0.8553191489361702,\n",
       " 0.4727272727272727,\n",
       " 0.9666666666666667,\n",
       " 0.8125,\n",
       " 0.851063829787234)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predic_stats(y_, y_pred, lr_decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = base.predict(x_)\n",
    "y_score = base.decision_function(x_)\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_b = dict()\n",
    "tpr_b = dict()\n",
    "roc_auc_b = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_b[i], tpr_b[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_b[i] = auc(fpr_b[i], tpr_b[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_b[\"micro\"], tpr_b[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_b[\"micro\"] = auc(fpr_b[\"micro\"], tpr_b[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.78\n"
     ]
    }
   ],
   "source": [
    "average_precision_b = average_precision_score(y_true, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision_b))\n",
    "\n",
    "precision_b, recall_b, _ = precision_recall_curve(y_true, y_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HPS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(lr_decision, (y_score.shape[0],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_h = dict()\n",
    "tpr_h = dict()\n",
    "roc_auc_h = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_h[i], tpr_h[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_h[i] = auc(fpr_h[i], tpr_h[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_h[\"micro\"], tpr_h[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_h[\"micro\"] = auc(fpr_h[\"micro\"], tpr_h[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.77\n"
     ]
    }
   ],
   "source": [
    "average_precision_h = average_precision_score(y_true, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision_h))\n",
    "\n",
    "precision_h, recall_h, _ = precision_recall_curve(y_true, y_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rbf kernel svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = grclf_svm.predict(x_)\n",
    "y_score = grclf_svm.decision_function(x_)\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_svcrbf = dict()\n",
    "tpr_svcrbf = dict()\n",
    "roc_auc_svcrbf = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_svcrbf[i], tpr_svcrbf[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_svcrbf[i] = auc(fpr_svcrbf[i], tpr_svcrbf[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_svcrbf[\"micro\"], tpr_svcrbf[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_svcrbf[\"micro\"] = auc(fpr_svcrbf[\"micro\"], tpr_svcrbf[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k nearest neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = grclf_knn.predict(x_)\n",
    "y_score = grclf_knn.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_knn = dict()\n",
    "tpr_knn = dict()\n",
    "roc_auc_knn = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_knn[i], tpr_knn[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_knn[i] = auc(fpr_knn[i], tpr_knn[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_knn[\"micro\"], tpr_knn[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_knn[\"micro\"] = auc(fpr_knn[\"micro\"], tpr_knn[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = grclf_rf.predict(x_)\n",
    "y_score = grclf_rf.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_rf = dict()\n",
    "tpr_rf = dict()\n",
    "roc_auc_rf = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_rf[i], tpr_rf[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_rf[i] = auc(fpr_rf[i], tpr_rf[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_rf[\"micro\"], tpr_rf[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_rf[\"micro\"] = auc(fpr_rf[\"micro\"], tpr_rf[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gaussian naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = clf_gnb.predict(x_)\n",
    "y_score = clf_gnb.predict_proba(x_)\n",
    "y_score = y_score[:,1] # take positive class\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_.astype(int)\n",
    "y_true = label_binarize(y_true, classes=[0, 1])\n",
    "n_classes = y_true.shape[1]\n",
    "y_score = np.reshape(y_score, (y_score.shape[0],1))\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr_nb = dict()\n",
    "tpr_nb = dict()\n",
    "roc_auc_nb = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr_nb[i], tpr_nb[i], _ = roc_curve(y_true[:, i], y_score[:, i])\n",
    "    roc_auc_nb[i] = auc(fpr_nb[i], tpr_nb[i])\n",
    "    \n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr_nb[\"micro\"], tpr_nb[\"micro\"], _ = roc_curve(y_true.ravel(), y_score.ravel())\n",
    "roc_auc_nb[\"micro\"] = auc(fpr_nb[\"micro\"], tpr_nb[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure()\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(10,8)\n",
    "lw = 4\n",
    "plt.rc('xtick', labelsize=40)\n",
    "plt.rc('ytick', labelsize=40)\n",
    "plt.plot(fpr_svcrbf[0], tpr_svcrbf[0], color='green',\n",
    "         lw=lw, label='RBF SVM (AUC=%0.3f)' % roc_auc_svcrbf[0])\n",
    "plt.plot(fpr_knn[0], tpr_knn[0], color='pink',\n",
    "         lw=lw, label='KNN (AUC=%0.3f)' % roc_auc_knn[0])\n",
    "plt.plot(fpr_rf[0], tpr_rf[0], color='brown',\n",
    "         lw=lw, label='RF (AUC=%0.3f)' % roc_auc_rf[0])\n",
    "plt.plot(fpr_nb[0], tpr_nb[0], color='orange',\n",
    "         lw=lw, label='GNB (AUC=%0.3f)' % roc_auc_nb[0])\n",
    "plt.plot(fpr_b[0], tpr_b[0], color='blue',\n",
    "         lw=lw, label='Base (AUC=%0.3f)' % roc_auc_b[0])\n",
    "plt.plot(fpr_h[0], tpr_h[0], color='red',\n",
    "         lw=lw, label='HPS (AUC=%0.3f)' % roc_auc_h[0])\n",
    "plt.plot([0, 1], [0, 1], color='grey', lw=lw, linestyle='--')\n",
    "plt.xlim([-0.05, 1.00])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('FPR', fontdict={'size': 40})\n",
    "plt.ylabel('TPR', fontdict={'size': 40})\n",
    "plt.title('ADNI2 pMCI vs sMCI', fontdict={'size': 40})\n",
    "plt.legend(loc=\"lower right\", prop={'size': 25})\n",
    "#ax.legend(loc='center left', bbox_to_anchor=(1,0.5), prop={'size': 30})\n",
    "plt.show()\n",
    "fig.savefig(path_results + 'adni2_mci_roc_multi.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}