Pearson.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "310337eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "from pandas.api.types import CategoricalDtype\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import zipfile\n",
    "import os\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model as lm\n",
    "\n",
    "# Plot settings\n",
    "plt.rcParams['figure.figsize'] = (12, 9)\n",
    "plt.rcParams['font.size'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed59d275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>wmparcwmlhbankssts</th>\n",
       "      <th>wmparcwmlhcaudalanteriorcingulate</th>\n",
       "      <th>wmparcwmlhcaudalmiddlefrontal</th>\n",
       "      <th>wmparcwmlhcuneus</th>\n",
       "      <th>wmparcwmlhentorhinal</th>\n",
       "      <th>wmparcwmlhfusiform</th>\n",
       "      <th>wmparcwmlhinferiorparietal</th>\n",
       "      <th>wmparcwmlhinferiortemporal</th>\n",
       "      <th>...</th>\n",
       "      <th>rhGausCurvprecentral</th>\n",
       "      <th>rhGausCurvprecuneus</th>\n",
       "      <th>rhGausCurvrostralanteriorcingulate</th>\n",
       "      <th>rhGausCurvrostralmiddlefrontal</th>\n",
       "      <th>rhGausCurvsuperiorfrontal</th>\n",
       "      <th>rhGausCurvsuperiorparietal</th>\n",
       "      <th>rhGausCurvsuperiortemporal</th>\n",
       "      <th>rhGausCurvsupramarginal</th>\n",
       "      <th>rhGausCurvtransversetemporal</th>\n",
       "      <th>rhGausCurvinsula</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2401.1</td>\n",
       "      <td>1887.8</td>\n",
       "      <td>7730.7</td>\n",
       "      <td>3174.8</td>\n",
       "      <td>652.2</td>\n",
       "      <td>6997.8</td>\n",
       "      <td>9180.7</td>\n",
       "      <td>5888.5</td>\n",
       "      <td>...</td>\n",
       "      <td>4870</td>\n",
       "      <td>3895</td>\n",
       "      <td>792</td>\n",
       "      <td>3429</td>\n",
       "      <td>8169</td>\n",
       "      <td>4072</td>\n",
       "      <td>4966</td>\n",
       "      <td>3453</td>\n",
       "      <td>356</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2236.6</td>\n",
       "      <td>2041.8</td>\n",
       "      <td>5735.7</td>\n",
       "      <td>2080.4</td>\n",
       "      <td>494.2</td>\n",
       "      <td>5334.0</td>\n",
       "      <td>8885.2</td>\n",
       "      <td>4370.8</td>\n",
       "      <td>...</td>\n",
       "      <td>4120</td>\n",
       "      <td>4187</td>\n",
       "      <td>627</td>\n",
       "      <td>3026</td>\n",
       "      <td>7022</td>\n",
       "      <td>3727</td>\n",
       "      <td>3905</td>\n",
       "      <td>3119</td>\n",
       "      <td>326</td>\n",
       "      <td>1830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>3725.3</td>\n",
       "      <td>2417.2</td>\n",
       "      <td>7204.6</td>\n",
       "      <td>2879.0</td>\n",
       "      <td>889.7</td>\n",
       "      <td>7162.9</td>\n",
       "      <td>11511.0</td>\n",
       "      <td>8157.2</td>\n",
       "      <td>...</td>\n",
       "      <td>5537</td>\n",
       "      <td>3892</td>\n",
       "      <td>979</td>\n",
       "      <td>4332</td>\n",
       "      <td>10448</td>\n",
       "      <td>4437</td>\n",
       "      <td>5390</td>\n",
       "      <td>3874</td>\n",
       "      <td>403</td>\n",
       "      <td>2380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>3614.6</td>\n",
       "      <td>2936.4</td>\n",
       "      <td>6269.4</td>\n",
       "      <td>3402.1</td>\n",
       "      <td>954.8</td>\n",
       "      <td>7135.5</td>\n",
       "      <td>10941.9</td>\n",
       "      <td>7832.2</td>\n",
       "      <td>...</td>\n",
       "      <td>5852</td>\n",
       "      <td>4924</td>\n",
       "      <td>818</td>\n",
       "      <td>4421</td>\n",
       "      <td>9437</td>\n",
       "      <td>4259</td>\n",
       "      <td>5906</td>\n",
       "      <td>3980</td>\n",
       "      <td>400</td>\n",
       "      <td>2368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>2999.8</td>\n",
       "      <td>1894.5</td>\n",
       "      <td>6517.6</td>\n",
       "      <td>3659.5</td>\n",
       "      <td>1061.2</td>\n",
       "      <td>6319.1</td>\n",
       "      <td>11548.2</td>\n",
       "      <td>8460.8</td>\n",
       "      <td>...</td>\n",
       "      <td>4570</td>\n",
       "      <td>4198</td>\n",
       "      <td>779</td>\n",
       "      <td>3322</td>\n",
       "      <td>8125</td>\n",
       "      <td>4571</td>\n",
       "      <td>4657</td>\n",
       "      <td>3690</td>\n",
       "      <td>363</td>\n",
       "      <td>2287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 409 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex  age  wmparcwmlhbankssts  wmparcwmlhcaudalanteriorcingulate  \\\n",
       "0    0   25              2401.1                             1887.8   \n",
       "1    1   26              2236.6                             2041.8   \n",
       "2    0   27              3725.3                             2417.2   \n",
       "3    0   32              3614.6                             2936.4   \n",
       "4    0   32              2999.8                             1894.5   \n",
       "\n",
       "   wmparcwmlhcaudalmiddlefrontal  wmparcwmlhcuneus  wmparcwmlhentorhinal  \\\n",
       "0                         7730.7            3174.8                 652.2   \n",
       "1                         5735.7            2080.4                 494.2   \n",
       "2                         7204.6            2879.0                 889.7   \n",
       "3                         6269.4            3402.1                 954.8   \n",
       "4                         6517.6            3659.5                1061.2   \n",
       "\n",
       "   wmparcwmlhfusiform  wmparcwmlhinferiorparietal  wmparcwmlhinferiortemporal  \\\n",
       "0              6997.8                      9180.7                      5888.5   \n",
       "1              5334.0                      8885.2                      4370.8   \n",
       "2              7162.9                     11511.0                      8157.2   \n",
       "3              7135.5                     10941.9                      7832.2   \n",
       "4              6319.1                     11548.2                      8460.8   \n",
       "\n",
       "   ...  rhGausCurvprecentral  rhGausCurvprecuneus  \\\n",
       "0  ...                  4870                 3895   \n",
       "1  ...                  4120                 4187   \n",
       "2  ...                  5537                 3892   \n",
       "3  ...                  5852                 4924   \n",
       "4  ...                  4570                 4198   \n",
       "\n",
       "   rhGausCurvrostralanteriorcingulate  rhGausCurvrostralmiddlefrontal  \\\n",
       "0                                 792                            3429   \n",
       "1                                 627                            3026   \n",
       "2                                 979                            4332   \n",
       "3                                 818                            4421   \n",
       "4                                 779                            3322   \n",
       "\n",
       "   rhGausCurvsuperiorfrontal  rhGausCurvsuperiorparietal  \\\n",
       "0                       8169                        4072   \n",
       "1                       7022                        3727   \n",
       "2                      10448                        4437   \n",
       "3                       9437                        4259   \n",
       "4                       8125                        4571   \n",
       "\n",
       "   rhGausCurvsuperiortemporal  rhGausCurvsupramarginal  \\\n",
       "0                        4966                     3453   \n",
       "1                        3905                     3119   \n",
       "2                        5390                     3874   \n",
       "3                        5906                     3980   \n",
       "4                        4657                     3690   \n",
       "\n",
       "   rhGausCurvtransversetemporal  rhGausCurvinsula  \n",
       "0                           356              1987  \n",
       "1                           326              1830  \n",
       "2                           403              2380  \n",
       "3                           400              2368  \n",
       "4                           363              2287  \n",
       "\n",
       "[5 rows x 409 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/Users/xinyuanhe/Desktop/413/completedata.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f1ca09f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, valid = train_test_split(df, test_size = 0.2)\n",
    "train_predictors = train.drop(labels = 'age', axis = 1)\n",
    "train_response = train[['age']]\n",
    "valid_predictors = valid.drop(labels = 'age', axis = 1)\n",
    "valid_response = valid[['age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "baff68e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>asegLeftThalamus</th>\n",
       "      <td>0.449621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asegRightThalamus</th>\n",
       "      <td>0.396217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rhGrayVollateralorbitofrontal</th>\n",
       "      <td>0.373744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rhGrayVolsuperiorfrontal</th>\n",
       "      <td>0.370095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lhGrayVolsuperiorfrontal</th>\n",
       "      <td>0.366656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rhGrayVolpericalcarine</th>\n",
       "      <td>0.004623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wmparcwmlhentorhinal</th>\n",
       "      <td>0.004188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wmparcwmrhentorhinal</th>\n",
       "      <td>0.004071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wmparcwmrhpericalcarine</th>\n",
       "      <td>0.002408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wmparcwmrhfrontalpole</th>\n",
       "      <td>0.001897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>407 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    age\n",
       "asegLeftThalamus               0.449621\n",
       "asegRightThalamus              0.396217\n",
       "rhGrayVollateralorbitofrontal  0.373744\n",
       "rhGrayVolsuperiorfrontal       0.370095\n",
       "lhGrayVolsuperiorfrontal       0.366656\n",
       "...                                 ...\n",
       "rhGrayVolpericalcarine         0.004623\n",
       "wmparcwmlhentorhinal           0.004188\n",
       "wmparcwmrhentorhinal           0.004071\n",
       "wmparcwmrhpericalcarine        0.002408\n",
       "wmparcwmrhfrontalpole          0.001897\n",
       "\n",
       "[407 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor = train.corr()\n",
    "cor = np.abs(cor[['age']]).sort_values('age', ascending = False)\n",
    "cor = cor.drop(labels = ['age', 'sex'], axis = 0)\n",
    "cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "71562a89",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mae(predicted, actual):\n",
    "    return float(np.mean(np.abs(actual - predicted)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "85b8126e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mae_list = []\n",
    "n_list = []\n",
    "for i in range(len(cor)-1):\n",
    "    n = i + 1\n",
    "    cols_select = np.append(np.array(cor[:n].index), 'sex')\n",
    "    x_train = train_predictors[cols_select]\n",
    "    MLR = lm.LinearRegression(fit_intercept = True)\n",
    "    MLR.fit(x_train, train_response)\n",
    "    x_valid = valid_predictors[cols_select]\n",
    "    predict_valid = MLR.predict(x_valid)\n",
    "    n_list.append(n)\n",
    "    mae_list.append(mae(predict_valid, valid_response))\n",
    "    \n",
    "n_array = np.array(n_list)[20:60]\n",
    "mae_array = np.array(mae_list)[20:60]\n",
    "\n",
    "plt.plot(n_array, mae_array)\n",
    "plt.xticks(n_array,n_array[::1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4536d67d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = 43\n",
    "cols_select = np.append(np.array(cor[:num].index), 'sex')\n",
    "MLR = lm.LinearRegression(fit_intercept = True)\n",
    "MLR.fit(train[cols_select], train[['age']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e4dad09c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asegLeftThalamus</th>\n",
       "      <th>asegRightThalamus</th>\n",
       "      <th>rhGrayVollateralorbitofrontal</th>\n",
       "      <th>rhGrayVolsuperiorfrontal</th>\n",
       "      <th>lhGrayVolsuperiorfrontal</th>\n",
       "      <th>rhGrayVolrostralmiddlefrontal</th>\n",
       "      <th>rhGrayVolinferiorparietal</th>\n",
       "      <th>lhGrayVollateralorbitofrontal</th>\n",
       "      <th>asegRightPutamen</th>\n",
       "      <th>lhGrayVolrostralmiddlefrontal</th>\n",
       "      <th>...</th>\n",
       "      <th>rhSurfAreainferiorparietal</th>\n",
       "      <th>rhGausCurvinferiorparietal</th>\n",
       "      <th>lhGrayVolparsorbitalis</th>\n",
       "      <th>rhGrayVolinferiortemporal</th>\n",
       "      <th>rhThickAvgrostralmiddlefrontal</th>\n",
       "      <th>rhGrayVolprecuneus</th>\n",
       "      <th>asegLeftAccumbensarea</th>\n",
       "      <th>rhThickAvgmedialorbitofrontal</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7559.5</td>\n",
       "      <td>7051.7</td>\n",
       "      <td>7579</td>\n",
       "      <td>27289</td>\n",
       "      <td>25764</td>\n",
       "      <td>11081</td>\n",
       "      <td>12677</td>\n",
       "      <td>7890</td>\n",
       "      <td>4775.7</td>\n",
       "      <td>11991</td>\n",
       "      <td>...</td>\n",
       "      <td>4332</td>\n",
       "      <td>4332</td>\n",
       "      <td>1883</td>\n",
       "      <td>7417</td>\n",
       "      <td>2.736</td>\n",
       "      <td>9936</td>\n",
       "      <td>540.5</td>\n",
       "      <td>2.724</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8563.6</td>\n",
       "      <td>8300.6</td>\n",
       "      <td>7405</td>\n",
       "      <td>27901</td>\n",
       "      <td>25790</td>\n",
       "      <td>10984</td>\n",
       "      <td>12967</td>\n",
       "      <td>7192</td>\n",
       "      <td>5450.0</td>\n",
       "      <td>10495</td>\n",
       "      <td>...</td>\n",
       "      <td>4064</td>\n",
       "      <td>4064</td>\n",
       "      <td>2326</td>\n",
       "      <td>8876</td>\n",
       "      <td>2.810</td>\n",
       "      <td>10926</td>\n",
       "      <td>477.8</td>\n",
       "      <td>3.356</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9594.8</td>\n",
       "      <td>8900.2</td>\n",
       "      <td>10379</td>\n",
       "      <td>32654</td>\n",
       "      <td>26342</td>\n",
       "      <td>12098</td>\n",
       "      <td>17073</td>\n",
       "      <td>9797</td>\n",
       "      <td>5359.7</td>\n",
       "      <td>13081</td>\n",
       "      <td>...</td>\n",
       "      <td>6457</td>\n",
       "      <td>6457</td>\n",
       "      <td>2528</td>\n",
       "      <td>13697</td>\n",
       "      <td>2.458</td>\n",
       "      <td>9879</td>\n",
       "      <td>337.6</td>\n",
       "      <td>2.808</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8708.1</td>\n",
       "      <td>8831.0</td>\n",
       "      <td>11135</td>\n",
       "      <td>28754</td>\n",
       "      <td>26289</td>\n",
       "      <td>13053</td>\n",
       "      <td>17692</td>\n",
       "      <td>10263</td>\n",
       "      <td>6067.4</td>\n",
       "      <td>12997</td>\n",
       "      <td>...</td>\n",
       "      <td>6271</td>\n",
       "      <td>6271</td>\n",
       "      <td>2215</td>\n",
       "      <td>12650</td>\n",
       "      <td>2.467</td>\n",
       "      <td>12542</td>\n",
       "      <td>518.3</td>\n",
       "      <td>2.657</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9288.5</td>\n",
       "      <td>9429.8</td>\n",
       "      <td>9840</td>\n",
       "      <td>25607</td>\n",
       "      <td>23443</td>\n",
       "      <td>9436</td>\n",
       "      <td>15616</td>\n",
       "      <td>9836</td>\n",
       "      <td>5894.9</td>\n",
       "      <td>11976</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>5837</td>\n",
       "      <td>2325</td>\n",
       "      <td>14261</td>\n",
       "      <td>2.538</td>\n",
       "      <td>10554</td>\n",
       "      <td>477.0</td>\n",
       "      <td>2.554</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1591</th>\n",
       "      <td>7902.8</td>\n",
       "      <td>7860.4</td>\n",
       "      <td>9276</td>\n",
       "      <td>27220</td>\n",
       "      <td>26954</td>\n",
       "      <td>10276</td>\n",
       "      <td>15927</td>\n",
       "      <td>8809</td>\n",
       "      <td>4390.3</td>\n",
       "      <td>10722</td>\n",
       "      <td>...</td>\n",
       "      <td>5754</td>\n",
       "      <td>5754</td>\n",
       "      <td>2066</td>\n",
       "      <td>12865</td>\n",
       "      <td>2.662</td>\n",
       "      <td>11543</td>\n",
       "      <td>338.8</td>\n",
       "      <td>2.840</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1592</th>\n",
       "      <td>8009.5</td>\n",
       "      <td>7688.6</td>\n",
       "      <td>8131</td>\n",
       "      <td>25091</td>\n",
       "      <td>23864</td>\n",
       "      <td>11439</td>\n",
       "      <td>13264</td>\n",
       "      <td>7571</td>\n",
       "      <td>5218.9</td>\n",
       "      <td>11182</td>\n",
       "      <td>...</td>\n",
       "      <td>4638</td>\n",
       "      <td>4638</td>\n",
       "      <td>1799</td>\n",
       "      <td>11224</td>\n",
       "      <td>2.548</td>\n",
       "      <td>10618</td>\n",
       "      <td>475.1</td>\n",
       "      <td>2.374</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1593</th>\n",
       "      <td>8732.0</td>\n",
       "      <td>8285.9</td>\n",
       "      <td>8990</td>\n",
       "      <td>25263</td>\n",
       "      <td>25523</td>\n",
       "      <td>10379</td>\n",
       "      <td>17165</td>\n",
       "      <td>8121</td>\n",
       "      <td>5535.1</td>\n",
       "      <td>11410</td>\n",
       "      <td>...</td>\n",
       "      <td>6511</td>\n",
       "      <td>6511</td>\n",
       "      <td>1856</td>\n",
       "      <td>12614</td>\n",
       "      <td>2.449</td>\n",
       "      <td>10398</td>\n",
       "      <td>465.9</td>\n",
       "      <td>2.532</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>10410.8</td>\n",
       "      <td>9782.7</td>\n",
       "      <td>10998</td>\n",
       "      <td>33126</td>\n",
       "      <td>32013</td>\n",
       "      <td>15802</td>\n",
       "      <td>15700</td>\n",
       "      <td>10444</td>\n",
       "      <td>5491.5</td>\n",
       "      <td>14878</td>\n",
       "      <td>...</td>\n",
       "      <td>6257</td>\n",
       "      <td>6257</td>\n",
       "      <td>1975</td>\n",
       "      <td>15322</td>\n",
       "      <td>2.489</td>\n",
       "      <td>11599</td>\n",
       "      <td>557.7</td>\n",
       "      <td>2.618</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>9163.0</td>\n",
       "      <td>7309.7</td>\n",
       "      <td>7315</td>\n",
       "      <td>24530</td>\n",
       "      <td>21331</td>\n",
       "      <td>11714</td>\n",
       "      <td>13890</td>\n",
       "      <td>7740</td>\n",
       "      <td>4332.3</td>\n",
       "      <td>10898</td>\n",
       "      <td>...</td>\n",
       "      <td>5662</td>\n",
       "      <td>5662</td>\n",
       "      <td>2065</td>\n",
       "      <td>11310</td>\n",
       "      <td>2.322</td>\n",
       "      <td>8185</td>\n",
       "      <td>294.2</td>\n",
       "      <td>2.350</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1596 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      asegLeftThalamus  asegRightThalamus  rhGrayVollateralorbitofrontal  \\\n",
       "0               7559.5             7051.7                           7579   \n",
       "1               8563.6             8300.6                           7405   \n",
       "2               9594.8             8900.2                          10379   \n",
       "3               8708.1             8831.0                          11135   \n",
       "4               9288.5             9429.8                           9840   \n",
       "...                ...                ...                            ...   \n",
       "1591            7902.8             7860.4                           9276   \n",
       "1592            8009.5             7688.6                           8131   \n",
       "1593            8732.0             8285.9                           8990   \n",
       "1594           10410.8             9782.7                          10998   \n",
       "1595            9163.0             7309.7                           7315   \n",
       "\n",
       "      rhGrayVolsuperiorfrontal  lhGrayVolsuperiorfrontal  \\\n",
       "0                        27289                     25764   \n",
       "1                        27901                     25790   \n",
       "2                        32654                     26342   \n",
       "3                        28754                     26289   \n",
       "4                        25607                     23443   \n",
       "...                        ...                       ...   \n",
       "1591                     27220                     26954   \n",
       "1592                     25091                     23864   \n",
       "1593                     25263                     25523   \n",
       "1594                     33126                     32013   \n",
       "1595                     24530                     21331   \n",
       "\n",
       "      rhGrayVolrostralmiddlefrontal  rhGrayVolinferiorparietal  \\\n",
       "0                             11081                      12677   \n",
       "1                             10984                      12967   \n",
       "2                             12098                      17073   \n",
       "3                             13053                      17692   \n",
       "4                              9436                      15616   \n",
       "...                             ...                        ...   \n",
       "1591                          10276                      15927   \n",
       "1592                          11439                      13264   \n",
       "1593                          10379                      17165   \n",
       "1594                          15802                      15700   \n",
       "1595                          11714                      13890   \n",
       "\n",
       "      lhGrayVollateralorbitofrontal  asegRightPutamen  \\\n",
       "0                              7890            4775.7   \n",
       "1                              7192            5450.0   \n",
       "2                              9797            5359.7   \n",
       "3                             10263            6067.4   \n",
       "4                              9836            5894.9   \n",
       "...                             ...               ...   \n",
       "1591                           8809            4390.3   \n",
       "1592                           7571            5218.9   \n",
       "1593                           8121            5535.1   \n",
       "1594                          10444            5491.5   \n",
       "1595                           7740            4332.3   \n",
       "\n",
       "      lhGrayVolrostralmiddlefrontal  ...  rhSurfAreainferiorparietal  \\\n",
       "0                             11991  ...                        4332   \n",
       "1                             10495  ...                        4064   \n",
       "2                             13081  ...                        6457   \n",
       "3                             12997  ...                        6271   \n",
       "4                             11976  ...                        5837   \n",
       "...                             ...  ...                         ...   \n",
       "1591                          10722  ...                        5754   \n",
       "1592                          11182  ...                        4638   \n",
       "1593                          11410  ...                        6511   \n",
       "1594                          14878  ...                        6257   \n",
       "1595                          10898  ...                        5662   \n",
       "\n",
       "      rhGausCurvinferiorparietal  lhGrayVolparsorbitalis  \\\n",
       "0                           4332                    1883   \n",
       "1                           4064                    2326   \n",
       "2                           6457                    2528   \n",
       "3                           6271                    2215   \n",
       "4                           5837                    2325   \n",
       "...                          ...                     ...   \n",
       "1591                        5754                    2066   \n",
       "1592                        4638                    1799   \n",
       "1593                        6511                    1856   \n",
       "1594                        6257                    1975   \n",
       "1595                        5662                    2065   \n",
       "\n",
       "      rhGrayVolinferiortemporal  rhThickAvgrostralmiddlefrontal  \\\n",
       "0                          7417                           2.736   \n",
       "1                          8876                           2.810   \n",
       "2                         13697                           2.458   \n",
       "3                         12650                           2.467   \n",
       "4                         14261                           2.538   \n",
       "...                         ...                             ...   \n",
       "1591                      12865                           2.662   \n",
       "1592                      11224                           2.548   \n",
       "1593                      12614                           2.449   \n",
       "1594                      15322                           2.489   \n",
       "1595                      11310                           2.322   \n",
       "\n",
       "      rhGrayVolprecuneus  asegLeftAccumbensarea  \\\n",
       "0                   9936                  540.5   \n",
       "1                  10926                  477.8   \n",
       "2                   9879                  337.6   \n",
       "3                  12542                  518.3   \n",
       "4                  10554                  477.0   \n",
       "...                  ...                    ...   \n",
       "1591               11543                  338.8   \n",
       "1592               10618                  475.1   \n",
       "1593               10398                  465.9   \n",
       "1594               11599                  557.7   \n",
       "1595                8185                  294.2   \n",
       "\n",
       "      rhThickAvgmedialorbitofrontal  sex  age  \n",
       "0                             2.724    0   25  \n",
       "1                             3.356    1   26  \n",
       "2                             2.808    0   27  \n",
       "3                             2.657    0   32  \n",
       "4                             2.554    0   32  \n",
       "...                             ...  ...  ...  \n",
       "1591                          2.840    1   26  \n",
       "1592                          2.374    1   39  \n",
       "1593                          2.532    1   36  \n",
       "1594                          2.618    0   21  \n",
       "1595                          2.350    0   38  \n",
       "\n",
       "[1596 rows x 45 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_final = np.append(cols_select, 'age')\n",
    "df[cols_final]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1218feb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[cols_final].to_csv('/Users/xinyuanhe/Desktop/413/selecteddata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "14f79998",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [41.9831161 ],\n",
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       "       [33.55562319],\n",
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       "       [53.6925799 ],\n",
       "       [54.12599136],\n",
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       "       [47.75299025],\n",
       "       [48.56161304],\n",
       "       [34.25113081],\n",
       "       [46.30212167],\n",
       "       [35.47999382],\n",
       "       [47.4614795 ],\n",
       "       [43.49965661],\n",
       "       [40.15763696],\n",
       "       [48.89110542],\n",
       "       [32.33548958],\n",
       "       [46.39242239],\n",
       "       [57.04407197],\n",
       "       [47.50575752]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLR.predict(valid[cols_select])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c5fc07a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.004246948380176"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae(MLR.predict(valid[cols_select]), valid[['age']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3940221f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>rhMeanCurvcaudalanteriorcingulate</th>\n",
       "      <th>rhMeanCurvcaudalmiddlefrontal</th>\n",
       "      <th>rhMeanCurvcuneus</th>\n",
       "      <th>rhMeanCurventorhinal</th>\n",
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       "      <th>CNBD_00011</th>\n",
       "      <td>0</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.140</td>\n",
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       "      <td>0.032</td>\n",
       "      <td>0.030</td>\n",
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       "      <td>0.032</td>\n",
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       "      <td>0.026</td>\n",
       "      <td>0.029</td>\n",
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       "    <tr>\n",
       "      <th>CNBD_00014</th>\n",
       "      <td>1</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.148</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.038</td>\n",
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       "    <tr>\n",
       "      <th>CNBD_00015</th>\n",
       "      <td>0</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.029</td>\n",
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       "      <th>CNBD_00029</th>\n",
       "      <td>0</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.032</td>\n",
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       "      <th>CNBD_00046</th>\n",
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       "      <td>0.121</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.145</td>\n",
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       "      <td>0.030</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.030</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>CNBD_02815</th>\n",
       "      <td>0</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.135</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNBD_02818</th>\n",
       "      <td>1</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNBD_02828</th>\n",
       "      <td>0</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.130</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNBD_02831</th>\n",
       "      <td>1</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.121</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNBD_10142</th>\n",
       "      <td>1</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>389 rows × 408 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            sex  rhMeanCurvcaudalanteriorcingulate  \\\n",
       "subject_ID                                           \n",
       "CNBD_00011    0                              0.118   \n",
       "CNBD_00014    1                              0.119   \n",
       "CNBD_00015    0                              0.124   \n",
       "CNBD_00029    0                              0.133   \n",
       "CNBD_00046    1                              0.121   \n",
       "...         ...                                ...   \n",
       "CNBD_02815    0                              0.137   \n",
       "CNBD_02818    1                              0.140   \n",
       "CNBD_02828    0                              0.132   \n",
       "CNBD_02831    1                              0.123   \n",
       "CNBD_10142    1                              0.131   \n",
       "\n",
       "            rhMeanCurvcaudalmiddlefrontal  rhMeanCurvcuneus  \\\n",
       "subject_ID                                                    \n",
       "CNBD_00011                          0.116             0.137   \n",
       "CNBD_00014                          0.120             0.147   \n",
       "CNBD_00015                          0.122             0.149   \n",
       "CNBD_00029                          0.120             0.140   \n",
       "CNBD_00046                          0.125             0.140   \n",
       "...                                   ...               ...   \n",
       "CNBD_02815                          0.114             0.151   \n",
       "CNBD_02818                          0.102             0.123   \n",
       "CNBD_02828                          0.105             0.141   \n",
       "CNBD_02831                          0.110             0.145   \n",
       "CNBD_10142                          0.110             0.133   \n",
       "\n",
       "            rhMeanCurventorhinal  rhMeanCurvfusiform  \\\n",
       "subject_ID                                             \n",
       "CNBD_00011                 0.136               0.123   \n",
       "CNBD_00014                 0.121               0.130   \n",
       "CNBD_00015                 0.118               0.133   \n",
       "CNBD_00029                 0.093               0.135   \n",
       "CNBD_00046                 0.131               0.137   \n",
       "...                          ...                 ...   \n",
       "CNBD_02815                 0.121               0.128   \n",
       "CNBD_02818                 0.141               0.123   \n",
       "CNBD_02828                 0.129               0.120   \n",
       "CNBD_02831                 0.114               0.119   \n",
       "CNBD_10142                 0.101               0.122   \n",
       "\n",
       "            rhMeanCurvinferiorparietal  rhMeanCurvinferiortemporal  \\\n",
       "subject_ID                                                           \n",
       "CNBD_00011                       0.122                       0.117   \n",
       "CNBD_00014                       0.122                       0.151   \n",
       "CNBD_00015                       0.126                       0.130   \n",
       "CNBD_00029                       0.137                       0.128   \n",
       "CNBD_00046                       0.136                       0.138   \n",
       "...                                ...                         ...   \n",
       "CNBD_02815                       0.120                       0.143   \n",
       "CNBD_02818                       0.125                       0.134   \n",
       "CNBD_02828                       0.120                       0.109   \n",
       "CNBD_02831                       0.122                       0.134   \n",
       "CNBD_10142                       0.132                       0.131   \n",
       "\n",
       "            rhMeanCurvisthmuscingulate  rhMeanCurvlateraloccipital  ...  \\\n",
       "subject_ID                                                          ...   \n",
       "CNBD_00011                       0.123                       0.140  ...   \n",
       "CNBD_00014                       0.132                       0.148  ...   \n",
       "CNBD_00015                       0.111                       0.152  ...   \n",
       "CNBD_00029                       0.126                       0.149  ...   \n",
       "CNBD_00046                       0.145                       0.145  ...   \n",
       "...                                ...                         ...  ...   \n",
       "CNBD_02815                       0.131                       0.135  ...   \n",
       "CNBD_02818                       0.129                       0.138  ...   \n",
       "CNBD_02828                       0.129                       0.130  ...   \n",
       "CNBD_02831                       0.119                       0.121  ...   \n",
       "CNBD_10142                       0.121                       0.139  ...   \n",
       "\n",
       "            rhGausCurvprecentral  rhGausCurvprecuneus  \\\n",
       "subject_ID                                              \n",
       "CNBD_00011                 0.032                0.030   \n",
       "CNBD_00014                 0.028                0.036   \n",
       "CNBD_00015                 0.029                0.032   \n",
       "CNBD_00029                 0.032                0.033   \n",
       "CNBD_00046                 0.030                0.032   \n",
       "...                          ...                  ...   \n",
       "CNBD_02815                 0.035                0.027   \n",
       "CNBD_02818                 0.034                0.026   \n",
       "CNBD_02828                 0.026                0.029   \n",
       "CNBD_02831                 0.028                0.027   \n",
       "CNBD_10142                 0.028                0.031   \n",
       "\n",
       "            rhGausCurvrostralanteriorcingulate  \\\n",
       "subject_ID                                       \n",
       "CNBD_00011                               0.028   \n",
       "CNBD_00014                               0.027   \n",
       "CNBD_00015                               0.029   \n",
       "CNBD_00029                               0.033   \n",
       "CNBD_00046                               0.029   \n",
       "...                                        ...   \n",
       "CNBD_02815                               0.047   \n",
       "CNBD_02818                               0.039   \n",
       "CNBD_02828                               0.026   \n",
       "CNBD_02831                               0.027   \n",
       "CNBD_10142                               0.031   \n",
       "\n",
       "            rhGausCurvrostralmiddlefrontal  rhGausCurvsuperiorfrontal  \\\n",
       "subject_ID                                                              \n",
       "CNBD_00011                           0.039                      0.034   \n",
       "CNBD_00014                           0.038                      0.029   \n",
       "CNBD_00015                           0.039                      0.036   \n",
       "CNBD_00029                           0.037                      0.033   \n",
       "CNBD_00046                           0.033                      0.031   \n",
       "...                                    ...                        ...   \n",
       "CNBD_02815                           0.033                      0.032   \n",
       "CNBD_02818                           0.031                      0.027   \n",
       "CNBD_02828                           0.032                      0.028   \n",
       "CNBD_02831                           0.030                      0.027   \n",
       "CNBD_10142                           0.035                      0.030   \n",
       "\n",
       "            rhGausCurvsuperiorparietal  rhGausCurvsuperiortemporal  \\\n",
       "subject_ID                                                           \n",
       "CNBD_00011                       0.032                       0.029   \n",
       "CNBD_00014                       0.030                       0.028   \n",
       "CNBD_00015                       0.033                       0.028   \n",
       "CNBD_00029                       0.031                       0.028   \n",
       "CNBD_00046                       0.031                       0.028   \n",
       "...                                ...                         ...   \n",
       "CNBD_02815                       0.033                       0.025   \n",
       "CNBD_02818                       0.033                       0.027   \n",
       "CNBD_02828                       0.028                       0.021   \n",
       "CNBD_02831                       0.027                       0.021   \n",
       "CNBD_10142                       0.026                       0.026   \n",
       "\n",
       "            rhGausCurvsupramarginal  rhGausCurvtransversetemporal  \\\n",
       "subject_ID                                                          \n",
       "CNBD_00011                    0.026                         0.029   \n",
       "CNBD_00014                    0.028                         0.037   \n",
       "CNBD_00015                    0.032                         0.030   \n",
       "CNBD_00029                    0.032                         0.041   \n",
       "CNBD_00046                    0.025                         0.037   \n",
       "...                             ...                           ...   \n",
       "CNBD_02815                    0.025                         0.028   \n",
       "CNBD_02818                    0.030                         0.033   \n",
       "CNBD_02828                    0.024                         0.030   \n",
       "CNBD_02831                    0.025                         0.028   \n",
       "CNBD_10142                    0.028                         0.109   \n",
       "\n",
       "            rhGausCurvinsula  \n",
       "subject_ID                    \n",
       "CNBD_00011             0.034  \n",
       "CNBD_00014             0.038  \n",
       "CNBD_00015             0.029  \n",
       "CNBD_00029             0.032  \n",
       "CNBD_00046             0.030  \n",
       "...                      ...  \n",
       "CNBD_02815             0.028  \n",
       "CNBD_02818             0.021  \n",
       "CNBD_02828             0.019  \n",
       "CNBD_02831             0.023  \n",
       "CNBD_10142             0.045  \n",
       "\n",
       "[389 rows x 408 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('/Users/xinyuanhe/Desktop/test_complete_data.csv').set_index('subject_ID')\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d14c18db",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.DataFrame(MLR.predict(test[cols_select]), index = test.index, columns = ['age'])\n",
    "result.to_csv('/Users/xinyuanhe/Desktop/result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1d084c3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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