Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f33bcba",
   "metadata": {
    "id": "8f33bcba"
   },
   "outputs": [],
   "source": [
    "#Basic imports\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import time\n",
    "import joblib\n",
    "\n",
    "import helpers as helpers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a620d1",
   "metadata": {
    "id": "46a620d1"
   },
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "378a854b",
   "metadata": {
    "id": "378a854b"
   },
   "outputs": [],
   "source": [
    "# Import data\n",
    "data1 = pd.read_excel('data.xlsx', 'Timepoint 1', header=1)\n",
    "data2 = pd.read_excel('data2.xlsx', 'Sheet1', header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ef5a083",
   "metadata": {
    "id": "5ef5a083",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data2['Date of birth'] = pd.to_datetime(data2['Date of birth'].astype(str), format='%d/%m/%Y')\n",
    "data2['Date of MRI scan'] = pd.to_datetime(data2['Date of MRI scan'].astype(str), format='%Y-%m-%d', errors='ignore')\n",
    "data2['Age'] = (data2['Date of MRI scan'] - data2['Date of birth'])/ np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7fc0feef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xxhtv8NtvvzF06FBCQ0NJTU3lk08+oVGjRvTr1w9QkxEvLy8WLVqEu7s7rq6u9OzZkyZNmlTl22fm7e1Nv379GD9+PCkpKcyfP5/mzZuXKxZ//PHHWblyJUOGDOH+++/n9OnTfPfdd1fUwFQltjvvvJNBgwbxyiuvEBsbS6dOnfj1119Zs2YNU6ZMueLc1fWvf/2L//3vf4wbN469e/cSFhbGypUr+eOPP5g/f/4VNUiVER4ezujRo/nkk0/IzMykT58+REZGVjgD+bBhw/j222/x9PSkbdu2REVFsXnz5iuG7oeHh2MwGHj33XfJzMzE0dGRm2++mT59+tCgQQPGjh3L5MmT0el0fPvtt1ckMFu2bGHSpEncd999tGzZkpKSEr799lsMBgP33HMPoL4/b775JtOnTyc2NpYRI0bg7u5OTEwMP/30E//61794/vnnAejatSvLly9n6tSpdO/eHTc3N+68805uu+02AgIC6Nu3L/7+/hw/fpyPPvqIoUOHVut7KYSZNoPPhLA9d955p+Lk5KTk5uZe9Zhx48Yp9vb2SlpamqIoinL+/HnlwQcfVNzd3RVPT09l3Lhxyh9//KEAyrJly8o99/Tp08qYMWOUgIAAxd7eXgkODlaGDRumrFy58rqxXbhwQXnkkUcUDw8PxdPTU3nkkUfMw5SvNQw+MjJSGT58uBIUFKQ4ODgoQUFByujRo5W///673PnXrFmjtG3bVrGzsyt3zgEDBlx1iPLVhsF///33yvTp0xU/Pz/F2dlZGTp0qBIXF3fF8+fOnasEBwcrjo6OSt++fZU9e/Zccc5rxfbPYfCKoijZ2dnKs88+qwQFBSn29vZKixYtlPfee08xmUzljgOUiRMnXhHT1Ybn/1NKSooyfvx4xcfHR3FwcFA6dOhQ4VD9yg6DVxRFyc/PVyZPnqw0bNhQcXV1Ve68804lISHhimHwFy9eNF/bzc1NiYiIUE6cOFFh7J999pnStGlTxWAwlBsS/8cffyi9evVSnJ2dlaCgIOWFF15QNm7cWO6YM2fOKI8++qjSrFkzxcnJSfH29lYGDRqkbN68+YrYf/zxR6Vfv36Kq6ur4urqqrRu3VqZOHGicvLkSfMxOTk5yoMPPqh4eXkpgPm9+9///qfcdNNNSsOGDRVHR0elWbNmyrRp05TMzMxKfd+EuBqdotTyij4h6pjVq1dz9913s2PHDvr27at1OEIIUS9JAiSEFeXn55cbJWU0GrntttvYs2cPycnJVhlBJYQQ4vqkBkgIK3r66afJz8+nd+/eFBYWsmrVKnbu3Mnbb78tyY8QQmhIWoCEsKKlS5cyd+5coqOjKSgooHnz5kyYMIFJkyZpHZoQQtRrkgAJIYQQot6ReYCEEEIIUe9IAiSEEEKIekeKoCtgMpk4d+4c7u7uFl8GQAghhBDWoSgK2dnZBAUFXbEg8T9JAlSBc+fOERISonUYQgghhKiGhIQEGjVqdM1jNE+APv74Y9577z2Sk5Pp1KkTCxYsoEePHhUee/ToUWbMmMHevXuJi4vj/fffZ8qUKeWOmT17NqtWreLEiRM4OzvTp08f3n333XILCF5P2fTqCQkJeHh4VPu1CSGEEKLmZGVlERISUqllUjRNgMrWfVm0aBE9e/Zk/vz5REREcPLkSfz8/K44Pi8vj6ZNm3Lffffx7LPPVnjO7du3M3HiRLp3705JSQkvv/wyt912G8eOHTMv/nc9Zd1eHh4ekgAJIYQQNqYy5SuaDoPv2bMn3bt3N6/ObDKZCAkJ4emnn+all1665nPDwsKYMmXKFS1A/3T+/Hn8/PzYvn07N910U6XiysrKwtPTk8zMTEmAhBBCCBtRlc9vzUaBFRUVsXfvXgYPHnwpGL2ewYMHExUVZbHrlK2I7e3tfdVjCgsLycrKKncTQgghRN2lWQKUlpaG0WjE39+/3H5/f3+Sk5Mtcg2TycSUKVPo27cv7du3v+pxs2fPxtPT03yTAmghhBCibtO8CNqaJk6cyJEjR9ixY8c1j5s+fTpTp041f11WRCWEEKJuMBqNFBcXax2GuEH29vYYDAaLnEuzBMjHxweDwUBKSkq5/SkpKQQEBNzw+SdNmsTatWv57bffrjsUztHREUdHxxu+phBCiNpFURSSk5PJyMjQOhRhIV5eXgQEBNzwPH2aJUAODg507dqVyMhIRowYAahdVpGRkTe0UKSiKDz99NP89NNPbNu2jSZNmlgoYiGEELamLPnx8/PDxcVFJre1YYqikJeXR2pqKgCBgYE3dD5Nu8CmTp3K2LFj6datGz169GD+/Pnk5uYyfvx4AMaMGUNwcDCzZ88G1MLpY8eOmbcTExM5cOAAbm5uNG/eHFC7vZYuXcqaNWtwd3c31xN5enri7OyswasUQgihBaPRaE5+GjZsqHU4wgLKPsdTU1Px8/O7oe4wTROgUaNGcf78eWbMmEFycjLh4eFs2LDBXBgdHx9fbirrc+fO0blzZ/PXc+bMYc6cOQwYMIBt27YBsHDhQgAGDhxY7lpfffUV48aNs+rrEUIIUXuU1fy4uLhoHImwpLL3s7i4+IYSIE3nAaqtZB4gIYSwfQUFBcTExNCkSROcnJy0DkdYyLXeV5uYB0gIIYQQQiuSAAkhhBCi3pEESAghhKhlxo0bh06nQ6fTYW9vj7+/P7feeitffvklJpOpSudavHgxXl5e1gn0GsaNG2ce5X0tv/32G3feeSdBQUHodDpWr15t9dhAEiAhhBCiVhoyZAhJSUnExsayfv16Bg0axDPPPMOwYcMoKSnROjyLyc3NpVOnTnz88cc1el1JgIQQohYymRT+PHOBP6LTSMsp1DocoQFHR0cCAgIIDg6mS5cuvPzyy6xZs4b169ezePFi83Hz5s2jQ4cOuLq6EhISwlNPPUVOTg4A27ZtY/z48WRmZppblF5//XUAvv32W7p164a7uzsBAQE8+OCD5jl2AC5evMhDDz2Er68vzs7OtGjRgq+++sr8eEJCAvfffz9eXl54e3szfPhwYmNjAXj99df5+uuvWbNmjfm6ZaO1/+n222/nzTff5O6777bo9+966vRSGEIIYYuOJGby/IqDnEjOBsDRTs/TNzdnwsDmGPQykd+NUBSF/GKjJtd2tjfc8ESMN998M506dWLVqlU8/vjjgLqQ+IcffkiTJk04c+YMTz31FC+88AKffPIJffr0Yf78+cyYMYOTJ08C4ObmBqjDyGfNmkWrVq1ITU1l6tSpjBs3jnXr1gHw2muvcezYMdavX4+Pjw/R0dHk5+ebnxsREUHv3r35/fffsbOz480332TIkCEcOnSI559/nuPHj5OVlWVOmq61KLkWJAESQohaJDEjn3Ff7SYtpxB3Rzu83RyIu5DHnF//Jj49j3dGdkQvSVC15RcbaTtjoybXPvZGBC4ON/6x27p1aw4dOmT+esqUKebtsLAw3nzzTZ588kk++eQTHBwc8PT0RKfTXbHM1KOPPmrebtq0KR9++CHdu3cnJycHNzc34uPj6dy5M926dTOfu8zy5csxmUx8/vnn5qTuq6++wsvLi23btnHbbbfh7OxMYWGhRZa3sgbpAhNCiFpCURQmLd1HWk4hrQPc+e2FQWx7fiD/vacjeh38sOcs8zf/rXWYQmOKopRrSdq8eTO33HILwcHBuLu788gjj3DhwgXy8vKueZ69e/dy55130rhxY9zd3RkwYACgTkIMMGHCBJYtW0Z4eDgvvPACO3fuND/34MGDREdH4+7ujpubG25ubnh7e1NQUMDp06et8KotT1qAhBCilth4NIX98Rm4Ohj4fGw3Grg6AHB/9xB0Opi28hALtkbTt7kPPZvK0g7V4Wxv4NgbEZpd2xKOHz9uXucyNjaWYcOGMWHCBN566y28vb3ZsWMHjz32GEVFRVedBTs3N5eIiAgiIiJYsmQJvr6+xMfHExERQVFREaDW5sTFxbFu3To2bdrELbfcwsSJE5kzZw45OTl07dqVJUuWXHFuX19fi7xOa5MESAghagGTSeH9TWrrzvi+TWjUoPwH133dQvgrJp2Ve8/y/MqDbHp2AE4W+kCtT3Q6nUW6obSyZcsWDh8+zLPPPguorTgmk4m5c+eal4764Ycfyj3HwcEBo7F83dOJEye4cOEC77zzDiEhIQDs2bPniuv5+voyduxYxo4dS//+/Zk2bRpz5syhS5cuLF++HD8/v6vOuFzRdWsT6QITQohaYOfpC5xMycbd0Y4n+jet8JjX72pHgIcTCen5fPbbmRqOUNS0wsJCkpOTSUxMZN++fbz99tsMHz6cYcOGMWbMGACaN29OcXExCxYs4MyZM3z77bcsWrSo3HnCwsLIyckhMjKStLQ08vLyaNy4MQ4ODubn/fzzz8yaNavc82bMmMGaNWuIjo7m6NGjrF27ljZt2gDw0EMP4ePjw/Dhw/n999+JiYlh27ZtTJ48mbNnz5qve+jQIU6ePElaWpp5bbZ/ysnJ4cCBAxw4cACAmJgYDhw4YO6KsxpFXCEzM1MBlMzMTK1DEULUE88u36+EvrhWeXnVoWset3r/WSX0xbVKq1fXKcmZ+TUUnW3Kz89Xjh07puTn2973aezYsQqgAIqdnZ3i6+urDB48WPnyyy8Vo9FY7th58+YpgYGBirOzsxIREaF88803CqBcvHjRfMyTTz6pNGzYUAGUmTNnKoqiKEuXLlXCwsIUR0dHpXfv3srPP/+sAMr+/fsVRVGUWbNmKW3atFGcnZ0Vb29vZfjw4cqZM2fM50xKSlLGjBmj+Pj4KI6OjkrTpk2VJ554wvzZmZqaqtx6662Km5ubAihbt26t8LVu3brV/Fovv40dO7bC46/1vlbl81sWQ62ALIYqhKhJeUUldHtzM3lFRn6c0JuuoVcfLqwoCvcuimJv3EXG9w1j5p3tajBS2yKLodZNshiqEELUEZuOpZBXZCS0oQtdGje45rE6nY5nB7cEYOlf8aRmFdREiELUOZIACSGExracUGffHdohsFIT5fVt3pBuoQ0oLDGxcLttDDkWoraRBEgIITRkMin8fioNgAEtKzd8WKfTMUVagYS4IZIACSGEho6eyyI9twhXBwOdr9P9dbnLW4EWbZcRYUJUlSRAQgihod9OnQegdzMfHOwq/ydZp9Mx+ZYWACzfHU9mfsVDjIUQFZMESAghNPRHdFn3l0+Vn9u/hQ+t/N3JLTKyfLeV50wRoo6RBEgIITRSYjRxICEDgO5Nqr5Stk6n47F+6pIIi/+IpcRosmR4QtRpkgAJIYRGTqZkk1dkxN3RjhZ+7tU6x13hQfi4OXAus4D1R5ItHKEQdZckQEIIoZF98RkAhDf2wqC//vD3ijjZG3i4VygAn++IQea2FaJyJAESQgiN7Iu7CFCl0V8VebhXKA52eg4mZLAv/qIlQhOizpMESAghNFKWrHRp7HVD5/Fxc+Tu8GAAPv895kbDErXAuHHj0Ol06HQ67O3t8ff359Zbb+XLL7/EZKpardfixYvx8vKyTqDXMG7cOEaMGHHd42bPnk337t1xd3fHz8+PESNGcPLkSavHJwmQEEJoIDOvmLgLeQB0DrmxFiCAx/qrxdAbjyaTkJ53w+cT2hsyZAhJSUnExsayfv16Bg0axDPPPMOwYcMoKSnROjyL2b59OxMnTuTPP/9k06ZNFBcXc9ttt5Gbm2vV60oCJIQQGjiWlAVAowbOeLrY3/D5Wvq707+FDyYFFu+MveHzCe05OjoSEBBAcHAwXbp04eWXX2bNmjWsX7+exYsXm4+bN28eHTp0wNXVlZCQEJ566ilycnIA2LZtG+PHjyczM9PcovT6668D8O2339KtWzfc3d0JCAjgwQcfJDU11Xzeixcv8tBDD+Hr64uzszMtWrTgq6++Mj+ekJDA/fffj5eXF97e3gwfPpzY2FgAXn/9db7++mvWrFljvu62bdsqfJ0bNmxg3LhxtGvXjk6dOrF48WLi4+PZu3evRb+f/yQJkBBCaOB4aQLUJvDaK1ZXxaOlQ+J/2JNAbmHdaSGwKEWBolxtbhYoUL/55pvp1KkTq1atMu/T6/V8+OGHHD16lK+//potW7bwwgsvANCnTx/mz5+Ph4cHSUlJJCUl8fzzzwNQXFzMrFmzOHjwIKtXryY2NpZx48aZz/vaa69x7Ngx1q9fz/Hjx1m4cCE+Pj7m50ZERODu7s7vv//OH3/8gZubG0OGDKGoqIjnn3+e+++/39yKlZSURJ8+fSr1GjMzMwHw9q761BBVYWfVswshhKhQWQtQWwsmQANa+NLEx5WYtFxW7U/kkdLRYeIyxXnwdpA21375HDi43vBpWrduzaFDh8xfT5kyxbwdFhbGm2++yZNPPsknn3yCg4MDnp6e6HQ6AgICyp3n0UcfNW83bdqUDz/8kO7du5OTk4Obmxvx8fF07tyZbt26mc9dZvny5ZhMJj7//HPzAr5fffUVXl5ebNu2jdtuuw1nZ2cKCwuvuO61mEwmpkyZQt++fWnfvn1Vvi1VJi1AQgihgbIWoLZBlkuA9HqdOen5ZmesDImvoxRFMScdAJs3b+aWW24hODgYd3d3HnnkES5cuEBe3rVrwfbu3cudd95J48aNcXd3Z8CAAQDEx6uzik+YMIFly5YRHh7OCy+8wM6dO83PPXjwINHR0bi7u+Pm5oabmxve3t4UFBRw+vTpar+2iRMncuTIEZYtW1btc1SWtAAJIUQNKzaaOJWi1mhYsgUI4N5ujZj760lOpeaw8/QF+jav+hIbdZq9i9oSo9W1LeD48eM0aaJ2d8bGxjJs2DAmTJjAW2+9hbe3Nzt27OCxxx6jqKgIF5eKr5mbm0tERAQREREsWbIEX19f4uPjiYiIoKioCIDbb7+duLg41q1bx6ZNm7jllluYOHEic+bMIScnh65du7JkyZIrzu3r61ut1zVp0iTWrl3Lb7/9RqNGjap1jqqQBEgIIWrY6fM5FBlNuDva0aiBs0XP7eFkzz1dG/FNVBxf/RErCdA/6XQW6YbSypYtWzh8+DDPPvssoLbimEwm5s6di16vdur88MMP5Z7j4OCA0Wgst+/EiRNcuHCBd955h5CQEAD27NlzxfV8fX0ZO3YsY8eOpX///kybNo05c+bQpUsXli9fjp+fHx4eFSfxFV23Ioqi8PTTT/PTTz+xbds2c3JnbdIFJoQQNexkcjYArQLcy3VlWMqY3mEARJ5IkSHxNqywsJDk5GQSExPZt28fb7/9NsOHD2fYsGGMGTMGgObNm1NcXMyCBQs4c+YM3377LYsWLSp3nrCwMHJycoiMjCQtLY28vDwaN26Mg4OD+Xk///wzs2bNKve8GTNmsGbNGqKjozl69Chr166lTZs2ADz00EP4+PgwfPhwfv/9d2JiYti2bRuTJ0/m7Nmz5useOnSIkydPkpaWRnFxcYWvc+LEiXz33XcsXboUd3d3kpOTSU5OJj8/39Lf0nIkARJCiBp2OlXt/mrh72aV8zf3c6N/Cx8UBb79M84q1xDWt2HDBgIDAwkLC2PIkCFs3bqVDz/8kDVr1mAwGADo1KkT8+bN491336V9+/YsWbKE2bNnlztPnz59ePLJJxk1ahS+vr7897//xdfXl8WLF7NixQratm3LO++8w5w5c8o9z8HBgenTp9OxY0duuukmDAaDuTbHxcWF3377jcaNGzNy5EjatGnDY489RkFBgblF6IknnqBVq1Z069YNX19f/vjjjwpf58KFC8nMzGTgwIEEBgaab8uXL7f0t7QcnSJVclfIysrC09OTzMzMqzbtCSFEdT21ZC/rDifz6tA2PN6/qVWuEXk8hce+3oOHkx1/vnwLLg71r+KhoKCAmJgYmjRpgpOTk9bhCAu51vtalc9vaQESQogaFl3aAtTMzzotQAADW/nR2NuFrIISVu/XqOhXiFpMEiAhhKhBJUYTMWnqFP/Nfa2XABn0Osb0VofEfy1D4oW4giRAQghRg+LT8yg2KjjbGwj2suwIsH+6r1sIzvYGTqZk8+eZdKteSwhbIwmQEELUoLLur6a+ruj1lh8BdjlPZ3tGdlFXiV+8U1aJF+JykgAJIUQNij6vJkDNrVj/c7mxfcIA2HQshbMX6+eQeOn+q1ss9X5KAiSEEDUo5rxa/9PUp2YSoJb+7vRt3hCTAt/9GV8j16wt7O3tAa67JISwLWXvZ9n7W131b1ykEEJoKO6C+sc7zMcyyyJUxtjeYfwRfYFlu+OZMrgFTvaGGru2lgwGA15eXqSmpgLq3DXWmHhS1AxFUcjLyyM1NRUvLy/zXEjVJQmQEELUoLh0tQUorGHNLcdwSxt/GjVw5uzFfNYcSGRU98Y1dm2tla1EXpYECdvn5eVVpRXmr0YSICGEqCF5RSWkZBUCNZsAlQ2Jf3vdCRbvjOP+biH1piVEp9MRGBiIn5/fVZdiELbD3t7+hlt+ykgCJIQQNSS+dF0uT2d7PF1urH6hqu7vFsK8TX9zPCmL3bEX6dHEu0avrzWDwWCxD05RN0gRtBBC1JDYtNL6n4Y1V/9TxsvFgbs7y5B4IcpIAiSEEDUk7oJa/xNag91flysbEr/xaArnMqy70rYQtZ0kQEIIUUPi0rVrAQJoHeBBr6beGE0KS/6SVeJF/SYJkBBC1BCtW4AAxpW2An2/K4GCYqNmcQihNUmAhBCihpTNARSqUQsQwOA2/gR5OpGeW8T/HZRV4kX9pXkC9PHHHxMWFoaTkxM9e/Zk165dVz326NGj3HPPPYSFhaHT6Zg/f/4Nn1MIIWpCidFEUmYBACHe2iVAdgY9j/QOA+DrKFklXtRfmiZAy5cvZ+rUqcycOZN9+/bRqVMnIiIirjphVV5eHk2bNuWdd9656iRIVT2nEELUhOSsAowmBQeDHl83R01jeaB7CI52eo4kZrEv/qKmsQihFU0ToHnz5vHEE08wfvx42rZty6JFi3BxceHLL7+s8Pju3bvz3nvv8cADD+DoWPEfkKqeUwghasLZi+qoq+AGzlZfBf56Grg6MDw8CICv/ojVNBYhtKJZAlRUVMTevXsZPHjwpWD0egYPHkxUVFStOacQQlhCWQLUqIGzxpGoyobEbziSTEpWgbbBCKEBzRKgtLQ0jEYj/v7+5fb7+/uTnJxco+csLCwkKyur3E0IISwpoXQIfG1JgNoFedIjzJsSk8KSP2VIvKh/NC+Crg1mz56Np6en+RYSEqJ1SEKIOuZSC5B2BdD/VNYKtHRXPIUlMiRe1C+aJUA+Pj4YDAZSUlLK7U9JSan2Kq/VPef06dPJzMw03xISEqp1fSGEuJqzF2tXCxDAbe38CfR0Ii2niPWHq9fyLoSt0iwBcnBwoGvXrkRGRpr3mUwmIiMj6d27d42e09HREQ8Pj3I3IYSwpNpWAwRgb9DzQPfGAHy/K17jaISoWZp2gU2dOpXPPvuMr7/+muPHjzNhwgRyc3MZP348AGPGjGH69Onm44uKijhw4AAHDhygqKiIxMREDhw4QHR0dKXPKYQQNa3EaCK5tNC4NnWBAdzfvRF6HfwVk87p8zlahyNEjbHT8uKjRo3i/PnzzJgxg+TkZMLDw9mwYYO5iDk+Ph69/lKOdu7cOTp37mz+es6cOcyZM4cBAwawbdu2Sp1TCCFqWlJm7ZkD6J8CPZ25ubUfm4+nsmxXPK8Mbat1SELUCJ0i04BeISsrC09PTzIzM6U7TAhxw/46c4FRn/5JaEMXtk8bpHU4V4g8nsJjX++hgYs9f758C452Bq1DEqJaqvL5LaPAhBDCysqWwAj0dNI4kooNaOlLoKcTF/OK2Xg05fpPEKIOkARICCGs7FymWgAd5FV7CqAvZ2fQc383dfqP7/+SYmhRP0gCJIQQVnYuozQB8qydCRDA/d1D0Okg6swFYtJytQ5HCKuTBEgIIawsKaO0C8yrdnaBAQR7OTOwpS8Ay3ZLK5Co+yQBEkIIKztXWgNUW7vAyozuoc4JtHLPWYpKTBpHI4R1SQIkhBBWlpRZ+7vAAG5u7YefuyMXcovYciJV63CEsCpJgIQQworyikrIyCsGancXGKjF0Hd3DgZg1b6zGkcjhHVJAiSEEFZ0rrT+x93RDg8ne42jub6RXRoBsPVkKum5RRpHI4T1SAIkhBBWVNb9Vdtbf8q0CnCnfbAHxUaF/zt4TutwhLAaSYCEEMKKzCPAann9z+XuKW0F+lG6wUQdJgmQEEJYUdkiqAEettECBHBXpyDs9DoOnc3kVEq21uEIYRWSAAkhhBWllCZA/rV0GYyKNHRzZGArPwB+3JeocTRCWIckQEIIYUXmBMijdq0Cfz33dlVHg/20/yxGk6yZLeoeSYCEEMKKUrIKAdvqAgMY1NoPT2d7UrIK2Xk6TetwhLA4SYCEEMKKLrUA2VYC5Ghn4K5OQQD8uFeKoUXdIwmQEEJYSYnRRFqO2gLkZ2NdYAAju6jdYBuOJpNbWKJxNEJYliRAQghhJWk5RZgUMOh1+LjaXgIUHuJFaEMXCopNRMrSGKKOkQRICCGspKz7y8/dEb1ep3E0VafT6bizo9oNJpMiirpGEiAhhLCSsjmA/Gys/udywzoFArD95HmyCoo1jkYIy5EESAghrCTVPAmi7XV/lWnl704LPzeKjCZ+PZqidThCWIwkQEIIYSXJNjoC7HI6nY5hpd1gaw9JN5ioOyQBEkIIKymbA8iWEyC41A2241QaF2WFeFFHSAIkhBBWYqtzAP1TM1832gZ6UGJS2HA0WetwhLAISYCEEMJKbHUZjIoM7ai2Aq0/IgmQqBskARJCCCupK11gAEPaBwAQdTpNRoOJOkESICGEsIKCYiOZ+WqiUBcSoGa+bjT3c6PYqLBVJkUUdYAkQEIIYQVl3V9O9no8nOw0jsYyItr5A7BR6oBEHSAJkBBCWMHlq8DrdLY3C3RFItqp3WDbTp6noNiocTRC3BhJgIQQwgpS6sAs0P/UIdiTIE8n8oqM7DiVpnU4QtwQSYCEEMIK6soQ+MvpdDpuK20FkuHwwtZJAiSEEFaQUgeWwajIbaV1QJuPp1BiNGkcjRDVJwmQEEJYQXIdGgJ/uR5h3ni52JORV8z+hAytwxGi2iQBEkIIK6iLNUAAdgY9N7XwBZDh8MKmSQIkhBBWcGkl+LqVAAEMaq0mQFskARI2TBIgIYSwgrJh8H7udasGCGBASz90OjiRnE1SZr7W4QhRLZIACSGEheUWlpBfOk+Obx1MgLxdHQgP8QLUOYGEsEWSAAkhhIWdz1Zbf5ztDbg61o1ZoP9pUCs/QOqAhO2SBEgIISwsLUdNgHzcHTSOxHpubq0mQH9Ep1FYIrNCC9sjCZAQQliYOQFyq3vdX2XaBnrg6+5IbpGR3TEXtQ5HiCqTBEgIISzsfE4RULcTIL1ex8CWpcPhT0o3mLA9kgAJIYSFpWXX/RYggIGldUCyLpiwRZIACSGEhZV1gfm61d0aIIDezRqi08HJlGxSswu0DkeIKpEESAghLOxSEXTdbgHydnWgbaAHAFGnL2gcjRBVIwmQEEJYWFppDZBvHe8CA+jX3AeQbjBheyQBEkIIC6svLUAAfUoToD+i01AUReNohKg8SYCEEMLC6ksRNED3sAY4GPScyywg9kKe1uEIUWmSAAkhhAXlFxnJLVInBvSp40XQAC4OdnRu7AWorUBC2ApJgIQQwoLKur8c7fS41dFlMP6p32XdYELYCkmAhBDCglIv6/7S6XQaR1MzyuqAos5cwGiSOiBhGyQBEkIIC6pPBdBlOjXyxN3Rjoy8Yo4nZWkdjhCVonkC9PHHHxMWFoaTkxM9e/Zk165d1zx+xYoVtG7dGicnJzp06MC6devKPZ6Tk8OkSZNo1KgRzs7OtG3blkWLFlnzJQghhFl9mQTxcnYGPd3CGgDwV0y6xtEIUTmaJkDLly9n6tSpzJw5k3379tGpUyciIiJITa14XZmdO3cyevRoHnvsMfbv38+IESMYMWIER44cMR8zdepUNmzYwHfffcfx48eZMmUKkyZN4ueff66plyWEqMfSsuv+OmAV6d7EG4DdkgAJG6FpAjRv3jyeeOIJxo8fb26pcXFx4csvv6zw+A8++IAhQ4Ywbdo02rRpw6xZs+jSpQsfffSR+ZidO3cyduxYBg4cSFhYGP/617/o1KnTdVuWhBDCEurDSvAV6VmWAMWmy3xAwiZolgAVFRWxd+9eBg8efCkYvZ7BgwcTFRVV4XOioqLKHQ8QERFR7vg+ffrw888/k5iYiKIobN26lb///pvbbrvNOi9ECCEucykBqj9dYAAdgr1wtNNzIbeI0+dztQ5HiOvSLAFKS0vDaDTi7+9fbr+/vz/JyckVPic5Ofm6xy9YsIC2bdvSqFEjHBwcGDJkCB9//DE33XTTVWMpLCwkKyur3E0IIaqjPhZBAzjY6QkP8QLUViAhajvNi6AtbcGCBfz555/8/PPP7N27l7lz5zJx4kQ2b9581efMnj0bT09P8y0kJKQGIxZC1CX1aR2wfyrrBtsldUDCBmg2S5ePjw8Gg4GUlJRy+1NSUggICKjwOQEBAdc8Pj8/n5dffpmffvqJoUOHAtCxY0cOHDjAnDlzrug+KzN9+nSmTp1q/jorK0uSICFEtZiXwahnLUBwqRBaEiBhCzRrAXJwcKBr165ERkaa95lMJiIjI+ndu3eFz+ndu3e54wE2bdpkPr64uJji4mL0+vIvy2AwYDKZrhqLo6MjHh4e5W5CCFFVBcVGsgtLgPpXBA3QpXEDDHodiRn5JGbkax2OENek6TztU6dOZezYsXTr1o0ePXowf/58cnNzGT9+PABjxowhODiY2bNnA/DMM88wYMAA5s6dy9ChQ1m2bBl79uzh008/BcDDw4MBAwYwbdo0nJ2dCQ0NZfv27XzzzTfMmzdPs9cphKgfyup/HAx6PJzqxzIYl3N1tKN9kAcHz2ayOyad4M7BWockxFVp+hs6atQozp8/z4wZM0hOTiY8PJwNGzaYC53j4+PLteb06dOHpUuX8uqrr/Lyyy/TokULVq9eTfv27c3HLFu2jOnTp/PQQw+Rnp5OaGgob731Fk8++WSNvz4hRP1SVv/j4+ZQb5bB+KfuYd4cPJvJrth0RkgCJGoxnSITNlwhKysLT09PMjMzpTtMCFFpm46l8MQ3e+jYyJOfJ/XTOhxNbDyazL+/3UtLfzd+fXaA1uGIeqYqn991bhSYEEJopb5Ogni5zo29ADiVmkN2QbG2wQhxDZIACSGEhZhHgNWzSRAv5+fuRKMGzigKHDqbqXU4QlyVJEBCCGEh0gKk6txYXRh1f/xFjSMR4uokARJCCAu5VARdzxOg0hmh98VnaBqHENciCZAQQljI+Xq6DMY/ldUB7Y+/KAujilqr/k1UIYSoPc6fhNNbIO1vcAuAwE7Q4jbQ2+b/ZmVdYPVxGYzLtQvyxMFOz8W8YuIu5BHm46p1SEJcQRIgIUTNyzoHG1+Goz9d+Zh/e4h4G5ra3hDqsiJoX/f6WwQN6sKo7YM82Befwf6Ei5IAiVrJNv/NEkLYrvg/4ZNeavKj00Ozm6HfVAh/CBw9IOUIfHs3HFiqdaRVUlhiJKug/i6D8U+XCqEztA1EiKuQFiAhRM2JjoRlD0FJPgSGw10LILDjpcdvexPWvwiHf4DVE8BYDF3HahZuVVwoLYC2N+jwdLbXOBrtXaoDytA0DiGuRlqAhBA1I/kwLH9YTX6a3wrj15dPfgBcvGHkp9B7kvr1uuchcV/Nx1oNZfU/DV0d6+0yGJcrawE6npRFfpFR42iEuJIkQEII68s5D9+PhuI8aDoQHlgKDi4VH6vTqS1BrYeBsQh+GAv5GTUZbbWY5wCq5/U/ZYI8nfBzd6TEpHA4USZEFLWPJEBCCOtSFLU7KzMBvJvCfYvB7jpJgk4HIz6BBk0gMx62vl0jod6I89kyCeLldDod4aXzAR06m6FpLEJURBIgIYR17V0M0ZvA4AijloBzg8o9z8kT7vxA3d79GSQfsVqIliCTIF6pYyNPAGkBErWSJEBCCOu5GAsbX1G3B88E/7ZVe37TAdB2OCgmtTi6Fk+qJy1AV+rQyAuAw7ImmKiFJAESQliHosC6aVCcC6F9oeeE6p3ntrfAzgnidkDMdsvGaEGX1gGTGqAyHYLVFqAzablkycrwopaRBEgIYR0n1sKpX0FvD8PmV392Z68Q6FI6FP63ORYLz9LMs0DX82UwLuft6kCjBs4AHJFuMFHLSAIkhLC84nxY/5K63fcZ8G15Y+frO1lNpGJ/h7ioG4/PCqQGqGJldUCHpBtM1DKSAAkhLO/PTyDrLHiGQP/nbvx8no0g/EF1+4/5N34+K5AWoIp1CPYCpA5I1D6SAAkhLCvnPPz+vrp9y4yrz/dTVX0mq/d/b4SLcZY5p4UUG01k5Kk1LtICVJ65BSgxQ9tAhPgHSYCEEJa1/V0oylZXdm9/r+XO69Mcmg4CFNj7leXOawFly2AY9Dq8ZBmMctqXFkInpOdzMbdI42iEuEQSICGE5aSdupSc3PZm9Qufr6b7Y+r9vm+gpNCy574Bl5bBcECvl2UwLufpbE+T0tXgZT4gUZtIAiSEsJzNr4OpBFoOgSY3Wf78LW8H9yDIuwDH/8/y56+m8zkyB9C1lA2HlwRI1CaSAAkhLCNupzr0XaeHwf+xzjUMdtBljLp9YKl1rlENaWWTIEoBdIUujQTL0DYQIS4jCZAQ4sYpCvz6qrrdZSz4tbbetTrer96f2QrZyda7ThWcl0kQr8ncAiQjwUQtIgmQEOLGHV0FiXvB3hUGTrfutRo2g0Y91OUxDq+07rUqKS1bLe71lS6wCrUN8gDgXGaBFEKLWkMSICHEjSkphM2lXV79poC7v/WvWdYKdGi59a9VCWlSA3RN7k72hDZUp0M4ei5L42iEUEkCJIS4MX9+Ahlx4BYAvSfWzDXb36PODJ18CFJP1Mw1r8GcALlLF9jVtCttBTp6TrrBRO0gCZAQovqyky+tz3Xrf8DBtWau6+INzW5Wt4//XDPXvAZpAbq+dkFqHdCxJGkBErWDJEBCiOqLfAOKciC4G3S4v2av3fYu9f5YbUiAZB2w62lrbgGSBEjUDtVKgM6cOWPpOIQQtubsXjiwRN2+/V3LT3p4Pa3uAJ0BUg7DhdM1e+3LlBhNXMwrLYKWYfBXVdYFduZ8DvlFRo2jEaKaCVDz5s0ZNGgQ3333HQUFBZaOSQhR25lMsP4FdbvTg9CoW83H4OJ9abJFDbvB0nOLUBTQ66CBi9QAXY2fuxM+bo6YFDieLK1AQnvVSoD27dtHx44dmTp1KgEBAfz73/9m165dlo5NCFFbHVoOiXvAwQ0Gz9QuDnM32BrNQiibA8jb1RGDLINxTe2kG0zUItVKgMLDw/nggw84d+4cX375JUlJSfTr14/27dszb948zp8/b+k4hRC1RU4qbCyd66f/c+AeoF0srYep9+f2Q1aSJiFcqv+R1p/rKUuAjslIMFEL3FCnvZ2dHSNHjmTFihW8++67REdH8/zzzxMSEsKYMWNIStLmD5IQwkoUBX6ZCvkXIaAD9Hla23jc/CC4q7p96ldNQihbBkPqf66vbCSYtACJ2uCGEqA9e/bw1FNPERgYyLx583j++ec5ffo0mzZt4ty5cwwfPtxScQohaoOD36uLkOrtYMRCMNhrHZG6QCrA3xs1ubwMga+8shagE8nZFBtNGkcj6rtqJUDz5s2jQ4cO9OnTh3PnzvHNN98QFxfHm2++SZMmTejfvz+LFy9m3759lo5XCKGV5COw9ll1e+BLagtQbdAyQr0/sxWKa35QxvlsWQesshp7u+DmaEdRiYnT53O0DkfUc9VKgBYuXMiDDz5IXFwcq1evZtiwYej/MQTWz8+PL774wiJBCiGqID8DLsapN5OFhhvnnIflD0NJATQfDP2es8x5LSGgA3gEQ3EexP5e45eXFqDK0+t1tA0sqwOSbjChLbvqPGnTpk00btz4iqRHURQSEhJo3LgxDg4OjB071iJBCiGuoygPdv0Pjv4ESQcv7bdzhqDO6mipdiOrt05XfgZ8dzdcjAHPxjDys5qf8+dadDq1FWjPl/D3Bmhxa41eXiZBrJq2QR7sik3n6LksRnbROhpRn1Xrr1izZs1IS0u7Yn96ejpNmjS54aCEEFVwajN83AM2v34p+bFzAoMDlORD/E7Y8BK83w5+fEKdwLCyMuLh62GQfBhc/WDManX+ndqm+WD1/sy2Gr/0pXXAJAGqjLayJpioJarVAqQoSoX7c3JycHJyuqGAhBBVsOcrdVSWYgLPELjpeXWGZDc/tfsr/QxER8LhFeq8PYd/UG/B3aDnk9B2ONhVULtiMsGRH2HDi5B3AVx84JFV0LBZzb/Gygjrp84KfSEaMhLAK6TGLn2pC0xqgCrj0lD4LBRFQaeTuZOENqqUAE2dOhUAnU7HjBkzcHFxMT9mNBr566+/CA8Pt2iAQoir+OtTWD9N3e78MNz+Hjhc+p1EbwCfFuqt15OQuA92faomNol7YNXjsO55tfuoUXc1gTIWQspRdX2t88fV8wR2glFLajSpqDInT3U4/NldajF0lzE1clmjSSE9V5bBqIoWfu7YG3RkFZRw9mI+Id4u13+SEFZQpQRo//79gNoCdPjwYRwcLv3H4+DgQKdOnXj++ectG6EQ4kqnt6rdWgD9psItM9RamGsJ7gJ3L4Jb34C9i2H3F5CTrM7qfGj5lcc7uEO/KdB7Itg7W/oVWF6zQWoCdLrmEqD03CJMivqt95ZlMCrFwU5PS393jp7L4ui5TEmAhGaqlABt3boVgPHjx/PBBx/g4eFhlaCEENeQEQ8rxoFiVNfhqkzyczk3PxjwgjqLc8Jf6gSCqScgKxHsXcAjSK2paXV77az3uZqmg2D7uxCzXe3Cq4FC7bLuL28XB+wMtagwvJZrF+RRmgBlMaR9oNbhiHqqWjVAX331laXjEEJUhqLA/z0DBRlql8+w96uW/FxOb4DQPuqtLmjUTV2bLO+CukJ8YCerX1KGwFePOiP0WZkRWmiq0gnQyJEjWbx4MR4eHowcOfKax65ateqGAxNCVODg93B6Cxgc4e5PwV4GHZgZ7NVi6L83qN1gNZkAuUv3V1W0DZK5gIT2Kt1m6+npaa7W9/T0vOZNCGEF+Rdh48vq9qDp4NNc23hqo6aD1PszW2vkcmnZMgdQdbQpnQwxOauAC6VJpBA1rdItQJd3e0kXmBAa+H2umgT5tYXeGi9CWls1K02A4qKgON/qxdvSBVY9bo52hDV0IfZCHseTsunXQr5/ouZVq2ovPz+fvLw889dxcXHMnz+fX3/VZjVmIeq8i3Hw1//U7VtngaFa5Xt1n09LcA9Uh/PH/2n1y11aB0w+wKvK3A2WJBMiCm1UKwEaPnw433zzDQAZGRn06NGDuXPnMnz4cBYuXGjRAIUQwLbZYCyCJgOg+S1aR1N76XQ12g12XiZBrDZZE0xorVoJ0L59++jfvz8AK1euJCAggLi4OL755hs+/PBDiwYoRL2XfgYO/aBuD369+qO+6ouybrDT1k+AzOuAySSIVXZpSQxJgIQ2qpUA5eXl4e7uDsCvv/7KyJEj0ev19OrVi7i4uCqd6+OPPyYsLAwnJyd69uzJrl27rnn8ihUraN26NU5OTnTo0IF169Zdcczx48e566678PT0xNXVle7duxMfH1+luISoNXbMV+f8aT5YncxQXFvTgep98iHIS7fqpcpqgHylC6zK1KHwcPp8DgXFRo2jEfVRtRKg5s2bs3r1ahISEti4cSO33XYbAKmpqVWaHHH58uVMnTqVmTNnsm/fPjp16kRERASpqakVHr9z505Gjx7NY489xv79+xkxYgQjRozgyJEj5mNOnz5Nv379aN26Ndu2bePQoUO89tprskaZsE2ZiXBgqbp90zRtY7EVbn7g00rdjo+y2mVMly2DITVAVefn7khDVwdMCpxMztY6HFEPVSsBmjFjBs8//zxhYWH07NmT3r17A2prUOfOnSt9nnnz5vHEE08wfvx42rZty6JFi3BxceHLL7+s8PgPPviAIUOGMG3aNNq0acOsWbPo0qULH330kfmYV155hTvuuIP//ve/dO7cmWbNmnHXXXfh5+dXnZcqhLb+WgimYgjtB417aR2N7Sib3DFup9UucTGvCKNJXRi6odQAVZlOp7usEFq6wUTNq1YCdO+99xIfH8+ePXvYsGGDef8tt9zC+++/X6lzFBUVsXfvXgYPHnwpGL2ewYMHExVV8X9tUVFR5Y4HiIiIMB9vMpn45ZdfaNmyJREREfj5+dGzZ09Wr159zVgKCwvJysoqdxNCc4U5sFcdbEDfydrGYmtC+6r3cX9Y7RJl9T8NXOyxl2UwqkUKoYWWqv1bGxAQQOfOndFftt5Ojx49aN26daWen5aWhtFoxN/fv9x+f39/kpOTK3xOcnLyNY9PTU0lJyeHd955hyFDhvDrr79y9913M3LkSLZv337VWGbPnl1uIseQkFq86rWoPw4shcJMaNgcmt+qdTS2JVRtlSbpIBRap3tF5gC6cZcKoWUovKh51ZpMJDc3l3feeYfIyEhSU1MxmUzlHj9z5oxFgquqsjiGDx/Os88+C0B4eDg7d+5k0aJFDBgwoMLnTZ8+nalTp5q/zsrKkiRIaMtkUru/AHo+WSMLe9Ypno3AKxQy4iBhl1WmDpAE6Ma1K02ATiRnYzQpGPQywlHUnGolQI8//jjbt2/nkUceITAw0LxERlX4+PhgMBhISUkptz8lJYWAgIAKnxMQEHDN4318fLCzs6Nt27bljmnTpg07duy4aiyOjo44OsofMVGLnN6iDn938oTwB7WOxjaF9lUToLidVkmAzJMgyhD4amvi44aTvZ68IiNxF3Jp6uumdUiiHqlWArR+/Xp++eUX+vbtW+0LOzg40LVrVyIjIxkxYgSgtuBERkYyadKkCp/Tu3dvIiMjmTJlinnfpk2bzEXYDg4OdO/enZMnT5Z73t9//01oaGi1YxWixu0tXW6m04Pg4KptLLYqtA8cXGq1QmjzHEBSAF1tBr2OVgEeHEzI4FhSliRAokZVKwFq0KAB3t7eN3zxqVOnMnbsWLp160aPHj2YP38+ubm5jB8/HoAxY8YQHBzM7NmzAXjmmWcYMGAAc+fOZejQoSxbtow9e/bw6aefms85bdo0Ro0axU033cSgQYPYsGED//d//8e2bdtuOF4hakRWEpxcr253G69tLLasbCRY4h4oLgB7y06FIV1gltE2sDQBOpfFsI5BWocj6pFqFRbMmjWLGTNmlFsPrDpGjRrFnDlzmDFjBuHh4Rw4cIANGzaYC53j4+NJSkoyH9+nTx+WLl3Kp59+SqdOnVi5ciWrV6+mffv25mPuvvtuFi1axH//+186dOjA559/zo8//ki/fv1uKFYhasz+79SJDxv3Ad9WWkdju7ybgpu/uoRI4l6Ln76sC0wmQbwxMiO00IpOURSlqk/q3Lkzp0+fRlEUwsLCsLe3L/f4vn37LBagFrKysvD09CQzM7NKEzsKccNMRvigE2QmwN2fQqdRWkdk21aMg6M/waBXYYBlJ5Ic+uHvHD2XxZfjunFza//rP0FUaF/8RUZ+shNfd0d2vzL4+k8Q4hqq8vldrS6wspodIYSFRUeqyY9zA2g7XOtobF9oXzUBivsDsGwCJF1gltE6wB2dTm1RS80uwM9dZu0XNaNaCdDMmTMtHYcQAsoXP1u4ZqVeKqsDStgFxmIw2F/7+EoymRQu5MgyGJbg4mBHEx9XzpzP5XhStiRAosZUe3KRjIwMPv/8c6ZPn056urrg4L59+0hMTLRYcELUK1nn4O/SmdW7jtU2lrrCtw04eUFxLiQdsthpM/OLKZFlMCxGZoQWWqhWAnTo0CFatmzJu+++y5w5c8jIyABg1apVTJ8+3ZLxCVF/7PsWFJPabSPFz5ah11+2LpjllsUo6/7ydLbH0c5gsfPWVzIjtNBCtRKgqVOnMm7cOE6dOlVulfU77riD3377zWLBCVFvmIywr3Tdr64y9N2iyhaRTfjLYqc8b67/kdYfS2gX5AnIoqiiZlUrAdq9ezf//ve/r9gfHBx81XW8hBDXcGoTZJ0FZ29oc6fW0dQtIT3V+4RdUPVBrxVKk/ofiyrrAotJyyWvqETjaER9Ua0EyNHRscIV0//++298fX1vOCgh6p29i9X7cCl+trjAcNDbQ26qujSGBaTJMhgW5evuiK+7I4qirgsmRE2oVgJ011138cYbb1BcXAyATqcjPj6eF198kXvuuceiAQpR52WehVMb1e2u4zQNpU6yd4KgcHU7YZdFTllWAySTIFqOFEKLmlatBGju3Lnk5OTg6+tLfn4+AwYMoHnz5ri7u/PWW29ZOkYh6ray4uew/uDTQuto6qZGPdR7C9UBpUkNkMXJjNCiplVrHiBPT082bdrEH3/8wcGDB8nJyaFLly4MHiyzeApRJcaSy4qfx2kaSp0W0gP+/NiCCZDUAFlau9IESAqhRU2pcgJkMplYvHgxq1atIjY2Fp1OR5MmTQgICEBRFHQ6nTXiFKJuit4E2efApaEUP1tTWSF0ylEozAZH9xs6Xdk6YJIAWU5ZF9iJpCxKjCbsDNWepk6ISqnST5iiKNx11108/vjjJCYm0qFDB9q1a0dcXBzjxo3j7rvvtlacQtRNe0pnfg5/EOzkw9RqPALBs7Ha1WiBhVHNXWBSBG0xoQ1dcXEwUFhiIvZCrtbhiHqgSgnQ4sWL+e2334iMjGT//v18//33LFu2jIMHD7J582a2bNnCN998Y61YhahbLsapLUAAXcZpGkq9EFJWB3RjhdCKolwqgpYEyGIMeh2tA9SWOakDEjWhSgnQ999/z8svv8ygQYOueOzmm2/mpZdeYsmSJRYLTog6bfdnaotE00Hg01zraOo+83xAN1YHlJlfTLFRnU9IiqAtq6wQWkaCiZpQpQTo0KFDDBky5KqP33777Rw8ePCGgxKizivKvVT83PNJbWOpL8wtQLvBZKr2acrqf2QZDMuTGaFFTapSApSeno6/v/9VH/f39+fixYs3HJQQdd6hH6AgExqEQYtbtY6mfvBvD/YuUJgJaSerfZqyBEi6vyzv8rmAFAvN2i3E1VQpATIajdjZXX3gmMFgoKREpjEX4poUBf76n7rd41+gl1aEGmGwg+Cu6vYN1AGdl0kQraZVgDsGvY4LuUWkZBVqHY6o46o0DF5RFMaNG4ejY8W/+IWF8gMrxHXF/g7nj4O9K4Q/pHU09UtID/X7n7ALuo6t1imkBch6nOwNNPd142RKNocTMwnwlGVhhPVUKQEaO/b6fzDGjBlT7WCEqBfKWn/CR4Ozl6ah1DvmQug/q30KSYCsq0MjTzUBOpvBrW2vXnIhxI2qUgL01VdfWSsOIeqHi7Fwcp263eNfmoZSLzXqrt5fiIa8dHDxrvIpJAGyrg7Bnqzce5bDiZlahyLqOJlqU4ia9Oei0qHvA8G3ldbR1D8u3uDdVN1O3FetU0gNkHW1D1ZHgh1OlEJoYV2SAAlRU3LOw97F6nafyZqGUq8Fd1PvE/dU6+nSAmRdbQM9MOh1pOUUkpxVoHU4og6TBEiImvLnJ1CSD0GdodnNWkdTfzUqTYDOSgJUGzk7GGjh5wbA4bPSDSasRxIgIWpC/kXY/bm63f95kEWDtWNuAdqrTklQBcVGE+l56krwkgBZz6VuMEmAhPVIAiRETfjjAyjMAr920OoOraOp3wI6gMER8tMh/UyVnpqeW4SiqOtWNXCRZTCspWMjSYCE9UkCJIS1ZSWpxc8At7wGevm105SdAwR2VLeruDJ8WfdXQ1cHDHppxbOWshagI4mZUggtrEb+EgthbdvfUWt/QnpBy6uvpSdqUFk32NndVXqa1P/UjEuF0EUkZUohtLAOSYCEsKZz+y8tejr4dan9qS2qWQgtCVDNcLK/rBBausGElUgCJIS1mIywdqo670/7eyG0t9YRiTJla4IlH4biyrcwyBxANadDWSG0jAQTViIJkBDWsudLOLcPHNwh4i2toxGXaxAGLg3BVKwmQZUkLUA1p6wQ+uDZDG0DEXWWJEBCWMP5v+HX19Ttm18F9wBt4xHl6XTVmhBREqCaEx7SAICDCRmYTFIILSxPEiAhLK2kEH58TC18bjJA1vyqrcrWBatCHZAkQDWndaA7jnZ6sgpKiLmQq3U4og6SBEgIS1IUWPssJB8CZ2+4+38y7L22alRaB1SFkWBSA1Rz7A1683D4A/EZ2gYj6iT5yyyEJf0+Fw4sAZ0eRn4GHoFaRySuJqiLep8RB7lplXqKtADVrPAQLwAOJGRoGoeomyQBEsJSdsyHLbPU7dv/Cy0GaxqOuA5nL/BpqW5Xohssr6iEnMISQBKgmiIJkLAmSYCEuFHGEvj1Vdg8U/36pmnQ4wltYxKVU4VC6LRsdQ0wJ3s9bo521oxKlOrc2AuA40lZFBQbtQ1G1DmSAAlxIy7Gwtd3ws4F6te3zFRHfQnbYK4Dun4CdD5HnS/I190RnUxoWSOCvZzxcXOkxKRw9JzMByQsSxIgIaojLx0iZ8FHPSB+pzrXz32Lof9UrSMTVXH5yvAm0zUPNdf/SAF0jdHpdOZusP1SCC0sTNpxhagsRVFHDB1YCoeWQ3Geur/JTTBsPjRspml4ohr824GdMxRmwYVT4NvqqodKAbQ2Ojf2YvPxFKkDEhYnCZAQ11JSBLG/w8l1cHI9ZCVeeiygA9z0ArS5U9b4slUGewgKh/gotRvsGglQqiRAmpBCaGEtkgAJ8U8mk9qtdegHOLYaCi6rPbB3VROezg9BWH9JfOqC4K5qApS4R31fryIlS60BCvBwqqnIBOqSGDodnL2Yz/nsQklAhcVIAiREmcJsdf2uXZ9BZsKl/a5+0Op2aD1U7e6yd9YuRmF5lVwZPiVLbQHykwSoRrk72dPCz42/U3LYF3+RiHayrIywDEmAhDAZYd/XalFzfrq6z9ED2t4FHUdBaF/QG7SNUVhPWSF0ylEoygMHlwoPK2sB8pcEqMZ1C/Pm75Qc9sSmSwIkLEYSIFG/XYyFHx+/tByCdzN1JFf7e6Slp77wbARu/pCTAkkHIbR3hYddSoCkC6amdQ9rwNK/4tkde1HrUEQdIsPgRf11cj0suklNfhw9YMi7MHEXdH5Ykp/6pBIrwxeWGLmYVwyAv7u0ANW0bqHeABxJzCS/SCZEFJYhCZCon/Z8BcsehMJMdVXwCX9AryfBII2i9dJ1JkRMLa3/cbDT4+ViX1NRiVKNGjgT4OFEiUmR0WDCYiQBEvVP1CewdgooJuj8CIxfD16NtY5KaOnyCRErkJp9qftLZoGueTqdjm5hDQDYG5eucTSirpAESNQv+76FjdPV7f7PwV0L1LlgRP0W1BnQqaP/slOueLhsBJh0f2mne5jaDSZ1QMJSJAES9cfpLfB/k9XtPk/Dza/JPD5C5eQBvq3V7QrqgGQEmPbKWoD2xV3EaFI0jkbUBZIAifoh/QysGK92e3UaDbfOkuRHlHeNOqBLcwDJCDCttA7wwM3RjuzCEk4mZ2sdjqgDakUC9PHHHxMWFoaTkxM9e/Zk165d1zx+xYoVtG7dGicnJzp06MC6deuueuyTTz6JTqdj/vz5Fo5a2IySQlg+Bgoy1Fl/h82X5Edc6RojwVKlBUhzBr2OLqFqK9AeqQMSFqB5ArR8+XKmTp3KzJkz2bdvH506dSIiIoLU1NQKj9+5cyejR4/mscceY//+/YwYMYIRI0Zw5MiRK4796aef+PPPPwkKCrL2yxC1WeQbkHIYXHxg1HdgLx9iogJlM0In7lcnx7xMSrbMAVQbdCtNgHbFSAIkbpzmCdC8efN44oknGD9+PG3btmXRokW4uLjw5ZdfVnj8Bx98wJAhQ5g2bRpt2rRh1qxZdOnShY8++qjccYmJiTz99NMsWbIEe3spcq23zmyDqNKfjeEfg4ckw+IqfNuAvQsUZUPa3+UekiLo2qGsEPqvmHQUReqAxI3RNAEqKipi7969DB482LxPr9czePBgoqKiKnxOVFRUueMBIiIiyh1vMpl45JFHmDZtGu3atbtuHIWFhWRlZZW7iTqgKA9+Li167joeWg3RNh5RuxnsSkeDccVw+LIiaFkHTFudG3vhaKfnfHYhp8/naB2OsHGaJkBpaWkYjUb8/f3L7ff39yc5ObnC5yQnJ1/3+HfffRc7OzsmT55cqThmz56Np6en+RYSElLFVyJqpe3vQkYceATDbbO0jkbYguAu6v1lhdB5RSVkF5QA0gWmNSd7A11Lu8GiTl/QOBph6zTvArO0vXv38sEHH7B48eJKT1g2ffp0MjMzzbeEhITrP0nUbinHYOcCdXvoXHB01zYeYRsqKIQumwXaxcGAm6PMFK61Ps0aArBTEiBxgzRNgHx8fDAYDKSklJ94LCUlhYCAilf8DQgIuObxv//+O6mpqTRu3Bg7Ozvs7OyIi4vjueeeIywsrMJzOjo64uHhUe4mbJiiqJMdKkZoPQxa3a51RMJWlBVCpxxTu1ApPweQzAKtvd7NfACIOnMBk8wHJG6ApgmQg4MDXbt2JTIy0rzPZDIRGRlJ794Vr8jcu3fvcscDbNq0yXz8I488wqFDhzhw4ID5FhQUxLRp09i4caP1XoyoPf7eqBY/Gxwg4i2toxG2xCMY3ALU5DnpAADJsgp8rdKxkScuDgYy8oo5IfMBiRugeXvu1KlTGTt2LN26daNHjx7Mnz+f3Nxcxo8fD8CYMWMIDg5m9uzZADzzzDMMGDCAuXPnMnToUJYtW8aePXv49NNPAWjYsCENGzYsdw17e3sCAgJo1apVzb44UfOMJfDrK+p274nQIEzTcISN0enUVqATa9U6oNA+5i4wmQOodrA36OnRxJttJ8+z83QabYOkxV5Uj+Y1QKNGjWLOnDnMmDGD8PBwDhw4wIYNG8yFzvHx8SQlJZmP79OnD0uXLuXTTz+lU6dOrFy5ktWrV9O+fXutXoKoTQ4tgwvR6pw//aZqHY2wRcGlM0KX1gHJMhi1T1kdkBRCixuheQsQwKRJk5g0aVKFj23btu2Kfffddx/33Xdfpc8fGxtbzciETTEWw/b/qtt9n1HXdxKiqsrqgM6qQ+FTskuXwXCXLrDaondTtQ5oV0w6JUYTdgbN/5cXNkh+akTdcfB7ddi7qy90f0zraIStKlsZPussZCdLC1At1DbIAw8ndV2ww4mZWocjbJQkQKJuKCmC395Tt/tOAQdXTcMRNszRHfzaqNtn98g6YLWQQa+jd2k32I5TaRpHI2yVJECibji4FDLiwdUPuj2qdTTC1pXWASmJey8tgyGjwGqVAS39ANj+93mNIxG2ShIgYftKiuC3Oep2v2fBwUXbeITtK60DMsbvJr9YXRhVWoBql5taqnVA+xMyyMwv1jgaYYskARK27+BSyExQ52/pNl7raERdUDojtC5pP3pMeLs64GRv0DgocblGDVxo5uuK0aSwM1q6wUTVSQIkbJvJBFEfq9t9ngZ7Z23jEXWDXxuwd8VQnEMz3TkCPaX1pzaSbjBxIyQBErYtejOk/Q2OHtBljNbRiLpCbzCvDB+ujybISxLr2mhAK19ATYAURZbFEFUjCZCwbVEfqfddxsi8P8KySleG76yLJkhagGqlnk28cbTTk5RZwKnUHK3DETZGEiBhu5IPQ8x20Bmg57+1jkbUNaWF0OH60wRKC1Ct5GRvoGdTdTj8b9INJqpIEiBhu6I+Ue/bDgevxtrGIuqe0kLoVrp4GrmaNA5GXM2Almo32LaTkgCJqpEESNim7GQ4vELd7l3xMipC3BDPYM7rvDHoFJqXRGsdjbiKgaV1QLti0skpLNE4GmFLJAEStmnXZ2AqhpBe0Kir1tGIOshkUthvbAZAYM5RjaMRV9PUx5UmPq4UGU38Lt1gogokARK2p7gA9n6lbveeqG0sos5Kyy1kX2kC5J52UONoxNXodDpuaa0Oh990PEXjaIQtkQRI2J5jayDvAng0glZ3aB2NqKPOZRRwQGkOgP7cXo2jEdcyuK0/AFtPpGI0yXB4UTmSAAnbs/tz9b7rODDYaRqKqLuSMvI5ZGqKET1kJUJmotYhiavoFtoAT2d7LuYVsy/+otbhCBshCZCwLcmH4ewu0NvJxIfCqs5lFpCHE4mOajcYZ3dpG5C4KjuDnkGlxdCbj0k3mKgcSYCEbdn9hXrf5k5w99c2FlGnJWXkA5Dq2UHdkbBbw2jE9ZR1g0kdkKgsSYCE7SjIgkM/qNvdHtM2FlHnnctUE6Ac39JRhgl/aRiNuJ6bWvpip9dx5nwuZ87LrNDi+iQBErbj0HIozgWfVhDWT+toRB13LqMAAF3jnuqOpIPqCERRK3k42dOrdFboTdINJipBEiBhGxQF9nypbnd7FHQ6beMRdV5SaQtQg6Dm4OqnzjuVdEDboMQ1RbRTu8HWH0nWOBJhCyQBErYh/k9IPQb2LtDpAa2jEXVcsdFEanYhAIFeLhDSQ31AusFqtYh2Aeh0cCAhg8TSGi4hrkYSIGEb9n2j3rcfCc5emoYi6r7kzAIUBRwMehq6OlyWAMlIsNrMz8OJ7qHeAGyQViBxHZIAidqvIBOO/qRudxmnaSiifkjKVGt9Ar2c0Ot1EFJaB5SwS+2OFbXW7R0CAFh/OEnjSERtJwmQqP0Or4SSfPBtDY26aR2NqAfK6n8CPZ3UHYHhoLeH3FS4GKtZXOL6hrRXE6C98RdJyZKidXF1kgCJ2m//t+p9lzFS/CxqRNkIsCBPZ3WHvRMEdlK3pRusVgv0dKZLYy8UBTYelW4wcXWSAInaLekQnNuv/vfdUYqfRc1IzMgD1C4ws7JuMJkRuta7vX0gAOukG0xcgyRAonYra/1pPRRcG2obi6g3EtLVLrDG3i6XdspIMJtR1g22Kyad86Wj+YT4J0mARO1VnK9Ofgiy7peoUQnpagtQSEUJUMpRKMzWICpRWSHeLnRs5IlJgV+PSTeYqJgkQKL2Or5WHQHm2RiaDtI6GlFPmEwKZy9W0ALkEQSeIaCYIHGfRtGJyirrBlt/WBIgUTFJgETttb907p/OD4FeflRFzUjJLqDIaMJOryOwrAi6TFkrUHxUzQcmquT20m6wqDMXSM8t0jgaURvJp4qondLPQMxvgA7CH9I6GlGPxF9Qu7+CGzhj0P9j1GHj3up93M4ajkpUVZiPK20DPTCaFJkUUVRIEiBRO+3/Tr1vfgt4hWgbi6hX4kvrf8p1f5UJ7aven90NxuIajEpUx13hQQCsOZCocSSiNpIESNQ+xhI4sFTd7vyItrGIeqfCAugyvq3ByQuK89TV4UWtdmcnNQHaFZtuntxSiDKSAInaJ3ozZCeBS0NodYfW0Yh6JqGiAugyej2E9lG3pRus1gv2cqZ7WAMUBdYelDmBRHmSAInap2zun06jwc5B21hEvVPWBRbSoIIECCQBsjF3hQcDsOagdIOJ8iQBErVLdgqcXK9uS/eX0MA1a4AAGpcmQPFRYDLVUFSiuoZ2CMROr+NIYhanz+doHY6oRSQBErXLwaWgGNVlB/xaax2NqGfyi4zmmYOvmgAFdgR7VyjIgPPHay44US3erg70b+EDwM8HzmkcjahNJAEStYeiwL7S7i9p/REaSLiotv54ONnh6WJf8UEG+0vzAUk3mE0YXtoN9vPBcyiKonE0oraQBEjUHnE7If00OLhBu7u1jkbUQ9ccAXY5qQOyKbe29cfJXk9MWi5HErO0DkfUEpIAidpj39fqfft7wNFN21hEvXTd+p8ylydA0qJQ67k62jG4jT8gcwKJSyQBErVD/kU4tkbd7jpW21hEvVXpBCi4KxgcICdZnbVc1Hpl3WD/d+gcRpMkrUISIFFbHPoBSgrAvwMEddE6GlFPVboLzN750s+prAtmEwa09MXT2Z6UrEKiTl/QOhxRC0gCJLSnKLC3tPur61jQ6a59vBBWkpCuToJ43QQIpA7IxjjY6bmzk7pC/I/7zmocjagNJAES2kvcC6lHwc4JOtyrdTSinlIUpfJdYHBpXbDYHVaMSljSPV0aAbD+SBLZBbKWW30nCZDQ3t7F6n3bEeDcQMtIRD2WllNEfrERnU5dQuG6GvcCvR1kxEF6jPUDFDcsPMSLZr6uFBSbWH9YVoiv7yQBEtoqzIYjq9RtKX4WGopJywXU5MfBrhJ/Gh3doFH30idvt2JkwlJ0Oh33dFVbgVZKN1i9JwmQ0NbhlVCcCz4toXFvraMR9VhMmrpMQlPfKkzB0HSgen9mm8XjEdZxd+dgdDrYFZNO/IU8rcMRGpIESGirbO6fLmOk+Flo6kxpC1BTH9fKP6nJgNInb5d1wWxEoKcz/ZqrS2NIMXT9JgmQ0E7SITi3H/T26srvQmgo5ryaADWpSgLUqJs6c3l+OqQcsVJkwtLuLe0G+3HfWUwyJ1C9JQmQ0M6+b9T7NsPA1UfbWES9V9YCVKUEyGB/aTSYdIPZjNvaBuDmaMfZi/nsik3XOhyhkVqRAH388ceEhYXh5OREz5492bVr1zWPX7FiBa1bt8bJyYkOHTqwbt0682PFxcW8+OKLdOjQAVdXV4KCghgzZgznzskqwLVKUZ46+SFAFyl+FtoymhTiLlQjAQJoWtYNts2yQQmrcXYwMLRD6ZxAe6UbrL7SPAFavnw5U6dOZebMmezbt49OnToRERFBampqhcfv3LmT0aNH89hjj7F//35GjBjBiBEjOHJEbX7Oy8tj3759vPbaa+zbt49Vq1Zx8uRJ7rrrrpp8WeJ6jq2GwkzwCr1URyGERhIv5lNsVHCw01duCPzlygqh46OgpNDisQnruLeb2g32y+EkcgpLNI5GaEHzBGjevHk88cQTjB8/nrZt27Jo0SJcXFz48ssvKzz+gw8+YMiQIUybNo02bdowa9YsunTpwkcffQSAp6cnmzZt4v7776dVq1b06tWLjz76iL179xIfH1+TL01cy+7P1fuuY0Gv+Y+hqOdOl44Aa9LQFb2+isX4fm3B1ReK8+DsbitEJ6yhW2gDmvq4kldk5P8OSg9BfaTpJ09RURF79+5l8ODB5n16vZ7BgwcTFVXx+jpRUVHljgeIiIi46vEAmZmZ6HQ6vLy8Kny8sLCQrKyscjdhRYn71NmfDQ7QeYzW0QjBmeoUQJfR6S4bDbbNckEJq9LpdDzQIwSA73fJP8f1kaYJUFpaGkajEX9//3L7/f39SU6ueJbO5OTkKh1fUFDAiy++yOjRo/Hw8KjwmNmzZ+Pp6Wm+hYSEVOPViErb/YV633YEuPlqGooQANGp2QC08K/CHECXk/mAbNI9XRphb9Bx6GwmRxIztQ5H1LA63fdQXFzM/fffj6IoLFy48KrHTZ8+nczMTPMtISGhBqOsZ/LS4chKdbvHE9rGIkSpUylqF1hzvxtMgBL3QYF8kNqKhm6ORLQLAGDZbmkFqm80TYB8fHwwGAykpKSU25+SkkJAQECFzwkICKjU8WXJT1xcHJs2bbpq6w+Ao6MjHh4e5W7CSg4sgZICCOhwaRkBITSkKAqnUtUEqKW/e/VO4hUC3s1AMULMbxaMTljbgz0aA7B6/znyiqQYuj7RNAFycHCga9euREZGmveZTCYiIyPp3bviZRF69+5d7niATZs2lTu+LPk5deoUmzdvpmHDhtZ5AaJqTKZL3V/dH5eZn0WtcD67kMz8YvS6atYAlWlxq3r/9wbLBCZqRK+mDQlt6EJOYQlrDyZpHY6oQZp3gU2dOpXPPvuMr7/+muPHjzNhwgRyc3MZP348AGPGjGH69Onm45955hk2bNjA3LlzOXHiBK+//jp79uxh0qRJgJr83HvvvezZs4clS5ZgNBpJTk4mOTmZoqIiTV6jKHV6C1yMAUdP6HCf1tEIAWBu/Qlt6IqTvaH6J2o5RL3/+1dZFsOG6PU6Rpe2An0dFYuiyMzQ9YXmCdCoUaOYM2cOM2bMIDw8nAMHDrBhwwZzoXN8fDxJSZey8j59+rB06VI+/fRTOnXqxMqVK1m9ejXt27cHIDExkZ9//pmzZ88SHh5OYGCg+bZz505NXqMotfsz9T78QXC4gf+0hbCgUylqAXS163/KhPYFB3fITVWXeBE2Y1S3EBzt9Bw9l8WeuItahyNqiJ3WAQBMmjTJ3ILzT9u2bbti33333cd991XcghAWFiYZfG2UFg1/b1S3uz+ubSxCXKasBajFjSZAdg7Q/GY4tkbtBmvU1QLRiZrQwNWBuzsHs2x3Aot3xtI9zFvrkEQN0LwFSNQTfy0EFLWbwKe51tEIYXYy+QaHwF/O3A22/sbPJWrU2D5hAGw4kkxSZr62wYgaIQmQsL68dNi/RN3uPVHbWIS4jKIonChNgNoEWmD0Z4vbAB0kH4bMxBs/n6gxbQI96NnEG6NJ4bs/47QOR9QASYCE9e39Ckry1aHvYf21jkYIs7MX88kpLMHeoKOZrwVagFx9Lk3vIKPBbM74vmEAfL8rgfwio7bBCKuTBEhYV0kR/PWput17kgx9F7XK8SR12Zvmfu7YGyz057BVWTeYJEC2ZnAbfxo1cCY9t4gf9siEuHWdJEDCuo6ugpxkcAuAdiO1jkaIci51f1VzAsSKlNUBndkORbmWO6+wOjuDnn8PaAbAp7+dodgo0xnUZZIACetRFIj6SN3u+S91lIwQtUhZC1CbAAvO/u7XFjwbg7FQTYKETbmvayN83BxJzMhnzQFZJb4ukwRIWE/MdrUY1M4Zuo7XOhohrmDRAugyOh20jFC3ZTSYzXGyN/B4/yYAfLItGqNJplWpqyQBEtbz2xz1vssj4CLzaojaJbugmJg0tYuqtSW7wABa36Hen/gFjLK+lK15qGdjPJzsOHM+l41Hk7UOR1iJJEDCOuL/gtjfQW8PfSZrHY0QVzh6Tu3+CvZyxsfN0bInD+sPzt6QdwHidlj23MLq3J3sGVc6L9C8TX9TIrVAdZIkQMI6fi9t/Qkfra6ULUQtc/hsJgAdgj0tf3KDPbS5U90++pPlzy+s7rH+TWngYk90ag4/7DmrdTjCCiQBEpZ37gCc+hV0euj3rNbRCFGhQ4mlCVAjKyRAAO1GqPfH/0+6wWyQp7M9T9/cAlBbgXIL5T2sayQBEpZX1vrT/l7wbqptLEJcxZFEK7YAAYTddKkbLPY361xDWNXDvUIJbehCWk4hn/52RutwhIVJAiQsK/W4+h8vQP/ntI1FiKvIzL9UAG21BMhgB23vUrcPr7TONYRVOdjpeSGiNaDOCyRrhNUtkgAJyyob+dXmLvBrrW0sQlxFWetPiLczDVytOD9Vx1Hq/bE1UJRnvesIq7mjQwBdQxuQX2xkxpqjKIoMi68rJAESlpN0CI6U/qd70zRtYxHiGvbGXQSgc0gD614opBd4NYaiHDi5zrrXElah0+l4++4O2Bt0bDqWwi+Hk7QOSViIJEDCciLfUO/b3wuBHbWNRYhrKEuAuoZaOQHS6y+1Ah1cZt1rCatpFeDOhIHNAXh51WHOZUhXWF0gCZCwjNgdEL0J9HYw6GWtoxHiqkwmhX3xNZQAAXR8QL0/HQnZMqmerXr65uZ0auRJVkEJk7/fT1GJzA1k6yQBEjdOUWDz6+p2l7HQsJmm4QhxLdHnc8guKMHZ3kDrAAvPAF0Rn+bQqAcoJjiwxPrXE1Zhb9DzwQOdcXeyY0/cRWasOSL1QDZOEiBx406ug7O7wd4FBrygdTRCXFNZ91enEE/sDDX0J7DruNKLfw0maTmwVWE+rnw4ujM6HSzbncC7G05KEmTDJAESN8ZkvFT702sCuAdoG48Q17ErJh2AbqE1uD5du7vB0RMy4iBmW81dV1jcoFZ+vDmiPQCLtp9m5s9HZakMGyUJkLgxe76E8yfAyUvW/BK1nqIoRJ2+AEDvZg1r7sIOLtDxfnV7z1c1d11hFQ/1DOX1O9ui08E3UXE8+NlfxF+QaQ5sjZ3WAQgblnsBtrypbt/8Kjh7aRqOENcTk5ZLclYBDgZ9zRRAX67rONj9mbpCfOZZ8GxUs9cXFjWubxP8PZx4fsVBdsWmM3jedkZ1D2FU9xDaBXmg0+nKHW8yKZzLzCc6NYfT53M5cz6HmLRcLuQUkVNYQn6xEVdHA0GezrQOcKd3s4YMaOmHs4NBo1dY90kCJKpvyxtQkAEBHaDbo1pHI8R1RZ1RW3/CG3vhZF/DHywB7dVV4mN/h12fwa3/qdnrC4u7vUMg7YI8efmnw+yITuPbP+P49s84vF0daOLjipO9nqISEzmFRmLTcskvNl7zfOm5kJCez18x6XwdFYeLg4G7OwfzRP+mhPm41tCrqj8kARLVk7hPLegEuGMO6OW/FFH7mbu/mtZg99flej2lJkB7F6sDBhzkQ83WNW7owneP92RndBrf/RVH5PFU0nOLSM8tuuJYe4OOsIauNPN1o5mfK0193PD3cMLNyQ4nez05BSXEp+dxMCGDyBOpnL2Yz5K/4vlhTwKP9WvK0zc3x9VRPrYtRadICfsVsrKy8PT0JDMzEw8PD63DqX1MJvjiVkjco07yNvJTrSMS4rqMJoVub27iYl4xP/y7Nz2a1GARdBmTERZ0hYsx6j8OPZ6o+RiEVRWVmDielMXZi/kUG0042ulxcjAQ1tCVkAbOlR55qCgKf55J55Nt0fx+Kg0Afw9H5t4XTr8WPtZ8CTatKp/fkkqKqjvwnZr8OLjBrW9oHY0QlXLwbAYX84pxd7KjS2MvbYLQG9RWoPXTYOeHal2QwV6bWIRVONjp6RTiRacQrxs6j06no3ezhvRq6s3m46nMWnuM+PQ8HvnyL6bc0pKnb26OXq+7/onEVckoMFE1Wedg46vq9sCXZNi7sBnbTqQCcFML35qb/6ciXR4BVz/IiIdDy7WLQ9gEnU7HrW39+fXZm3igewiKAu9v/ptHv95NbmGJ1uHZNEmAROUpCvw8GQozIbgr9JygdURCVNrWk+cBGNjKV9tA7J2hz9Pq9m9zwCgfYuL6nOwNvHNPR+bc1wknez3bTp5n9Gd/kpZTqHVoNksSIFF5B5ao630ZHGHEQjBID6qwDSlZBRxOzARggNYJEKijJp291Vqgg99rHY2wIfd2bcSyf/XG29WBQ2czuWfhThLSZQ6i6pAESFRO5lnYMF3dHvQy+LbSNh4hqmDDEXUR0i6NvfBzd9I4GsDRDfo9q25vfRuK5ANMVF54iBc/TuhDiLczcRfyeODTPyUJqgZJgMT1mUylXV9ZENztUvO9EDbil8NJANzRIVDjSC7T41/g2Riyz8Gfn2gdjbAxTXxc+fHJPjTzdSUxI1+SoGqQBEhc384P4HTkpa4vmfNH2JDU7AJ2x6rrf91emxIgeye45TV1e8d8yErSNBxhe/w8nPj+iV40lSSoWiQBEtcWuwMiZ6nbd/wXfFtqG48QVfTLoSQUBTqFeBHs5ax1OOW1v1cdUFCUDRtf1joaYYP8PJxYJklQtUgCJK4uOwVWPgqKETo+AF3Gah2REFW2Ys9ZAEZ2DtY4kgro9TDsfdDp4egqiN6sdUTCBpmTIB81CXrw8z9JyszXOqxaTxIgUTFjCfz4GOSkgG8bGDYPdDLplrAtR89lciwpCweDnrs6BWkdTsUCO12aUuL/pkB+hpbRCBvl5+HE0id6EdrQhYT0fB787C9Sswq0DqtWkwRIXElR4NdX1DWLHNzg/m9kzSJhk8pafwa39aOBq4PG0VzDoJehQRPITIB1z2sdjbBRAZ5qEhTs5UxMWi4Pfv6XzBN0DZIAiStFfQx/LVK3h38kdT/CJmUXFLNyr5oAPdC9scbRXIejG4z8DHQGOLwCDizVOiJho4K9nPn+iV4EejoRnZrDw5//xcUKFmYVkgCJfzryo9r6A3DrLGh3t7bxCFFNP+w5S05hCc393OhvC4tHhnRXl5cBtSssca+m4Qjb1bihC0se74mvuyMnkrN55Mu/yMwv1jqsWkcSIHFJ7B/w05Pqds8nZb4fYbOKjSa++iMGgPF9w9DZSv1a/+eh5e1gLIRlD0NmotYRCRvV1NeNpY/3pKGrA0cSsxj75S5yZO2wciQBEqq4nbB0FBiLoM2dEPG2FD0Lm/Xj3rOcvZiPj5sDIzs30jqcytPrYeSn4NNKnSDx2xGQm6Z1VMJGtfB357vHe+LlYs+BhAwe+eIvMvKkO6yMJEACTm+Bb0eqc5E0uUmtRZDJDoWNKiwxsmBLNAATBjbH2cHGfpadPODhH8GjEaT9Dd+MgJxUraMSNqpNoAffPtoTT2d79sdnMOp/f5Iio8MASYDEyfVqy09JPrS4DR78QV2tWggb9eWOWBIz8vFzd+ShnrW8+PlqvEJgzGpw9YOUw/DFbZB+RuuohI3q0MiTH/7dGz93R06mZHPvop3EXcjVOizNSQJUXykK7PkSlj98qdtr1BJJfoRNS84sYMGWUwC8dHtrnOxtrPXncj4t4NEN4BWqrhr/6UD4+1etoxI2qlWAOz9O6GOeJ+jeRVEcSczUOixNSQJUHxUXwM9Pw9pnwVQCHUfBvYvBrhbPkyLEdSiKwkurDpFXZKRbaAPuro0zP1dVw2bw2K/QqDsUZMLS+2DdNCjM0ToyYYNCvF1Y8WRvWge4cz67kPsWRfHLofq7Bp0kQPVN5llYfAfs/1adfn/w63D3/8Bgp3VkQtyQb6Li2HbyPI52emaP7GA7I7+uxz0Axv0C3Z9Qv971KXzSCw4uB5NJ29iEzfFzd2L5v3vTv4UP+cVGJi7dx5yNJykx1r+fJZ2iKIrWQdQ2WVlZeHp6kpmZiYeHh9bhWIbJBPu+hk0zoDALnLzg3i+h+S1aRybEDfvzzAUe/vwvSkwKM4a15dF+TbQOyTpOb4Gfn4HMePVr39bQexK0HymztYsqKTGaeGf9CT7foU4X0S20Ae+PCifE20XjyG5MVT6/JQGqQJ1LgNJOwf89A3F/qF8Hd4V7vgDvOvohIeqV40lZjP7sTzLyihnWMZAFozvXndafihTnw58LYcf76j8zAPYu0DJCnbi0+a3gYNsfYqLmrDmQyCs/HSGnsAR3RzteHdaG+7qGoNfb5u+QJEA3qM4kQJln4fd5sO8bMBWrfyRvmQE9/iXD3EWdcDAhg/GLd5OeW0SnEC+WPdHL9oa9V1dBJuz9GvZ8ARdjL+3X20FABwjpqdYO+bYC72aSFImrSkjPY8ryA+yNuwhA58ZezBrenvbBnhpHVnWSAN0gm0+ALpyGPz9REx9j6aRXzQfD0HnQIFTb2ISwAEVRWLn3LK+uPkJhiYmOjTz59jF1rpN6R1Hg3H44+hMcWw0Z8RUf5xmijijzCAKPQPAILt0OAvcgcPOTf4zqsRKjia/+iGX+5r/JLTKi08Ed7QN5+pbmtA6wnc9Bm0uAPv74Y9577z2Sk5Pp1KkTCxYsoEePHlc9fsWKFbz22mvExsbSokUL3n33Xe644w7z44qiMHPmTD777DMyMjLo27cvCxcupEWLFpWKxyYToMJsOLYG9i+B+J2X9of1V9cXCuunXWxCWFBMWi5vrj1G5Al1csCbW/vx4ejOuDlKIT+Koq4on7BLvZ3br06mWJBx/efq7cA9sDQpCr507xlc+nUwuPmrs1WLOislq4C31x1nzYFz5n0DW/kyukdjbm7th72hdr//NpUALV++nDFjxrBo0SJ69uzJ/PnzWbFiBSdPnsTPz++K43fu3MlNN93E7NmzGTZsGEuXLuXdd99l3759tG/fHoB3332X2bNn8/XXX9OkSRNee+01Dh8+zLFjx3BycrpuTDaRAJmMkHpMLYqMjoT4qEutPTo9NLsF+j4DTfprG6cQFmA0Kfx55gI/7Elg7aEkjCYFO72OZ29tyZMDmmGw0XqFGqEokHcBLkSr3eJZiZCVVHp/DrKT1JtSiVFAens1MfIMAc9G6oSNno1Kb6XbUoxdJ5xIzmJBZDTrjiRRliX4uDlya1t/bm3rR++mPrWyu9mmEqCePXvSvXt3PvroIwBMJhMhISE8/fTTvPTSS1ccP2rUKHJzc1m7dq15X69evQgPD2fRokUoikJQUBDPPfcczz//PACZmZn4+/uzePFiHnjggevGVKsSoJJCyEiAjFi1nz/1BCQdhJQjUJxX/tiGzSH8Qeg0Wv0jJYSNKig2Enchj/3xF9kXf5EtJ86TllNofnxgK19eHdqG5n7uGkZZhxhLIDdVXXy1LDHKKt3OvCxRUozXP5ezd/mEqCxRcvVVH3PxBucGYOdo/dclblhMWi7Ldsfz496zpOVcWkfMTq+jXZAHnRs3oIW/G0193Gjm64qvu6OmgxCq8vmtaZtxUVERe/fuZfr06eZ9er2ewYMHExUVVeFzoqKimDp1arl9ERERrF69GoCYmBiSk5MZPHiw+XFPT0969uxJVFRUpRIgqzl3AM7uVpOakoIr74vzIP+i+t9aXrq6XTbKoyL2rmrXVrOb1eHsDZvLAqbCpizafprz2YVk5BWTkVfExbwikjILSM4q4J//mnm52HN7+wAe7BFKh0a2V5xZqxnsLtUD0b3iY4wlkJOstiJlJKhdbZlnL7slqH+v8tPVW/Kha1/T3rU0GfICB3d1FnoHF3WwhvnmDAZ7tTZJbwe60nu93aV9ZfeO7tB6qKW/M/VeEx9Xpt/ehudubUXUmQtsPpZC5PEUzmUWcPBsJgfPlp9N2tnegI+7Aw1dHfFxc8DT2QFnBz0uDnY42RtwcTDgZKfHzqCnfbAn4SFe2rwwNE6A0tLSMBqN+Pv7l9vv7+/PiRMnKnxOcnJyhccnJyebHy/bd7Vj/qmwsJDCwkv/XWZmqm9oVtY1ko/qOPQLbH+36s+zcwGvxurNu4k6wsO/vbp9edFidrblYhWiBvxv02Eu5BZX+Jiro4F2gZ50bORJ17AG9Gra0Fx/YPHfTVE5Og/waqveKlKQean1KDOxfItS3gW1Fik/AzCps1nn5ABXKdquKo9gmChd/tbUOcCRzgGNeX5QCOcy8jmQkMGRc1nEpuUQeyGPxIv55BZCbg7EVeJ8j/VrQlPPlhaNsexvQ2U6t6RqEJg9ezb/+c9/rtgfEhKiQTQVyQZSgN1aByJEjToB/Kh1EMJGnICXpWXQlrw+H1630rmzs7Px9Lz2z4OmCZCPjw8Gg4GUlJRy+1NSUggICKjwOQEBAdc8vuw+JSWFwMDAcseEh4dXeM7p06eX61YzmUykp6fTsGHDuj2hmoVkZWUREhJCQkKC9jVTwkzel9pJ3pfaSd6X2qmq74uiKGRnZxMUdP06WE0TIAcHB7p27UpkZCQjRowA1OQjMjKSSZMmVfic3r17ExkZyZQpU8z7Nm3aRO/evQFo0qQJAQEBREZGmhOerKws/vrrLyZMmFDhOR0dHXF0LF+Q5+XldUOvrT7y8PCQPxy1kLwvtZO8L7WTvC+1U1Xel+u1/JTRvAts6tSpjB07lm7dutGjRw/mz59Pbm4u48ePB2DMmDEEBwcze/ZsAJ555hkGDBjA3LlzGTp0KMuWLWPPnj18+umnAOh0OqZMmcKbb75JixYtzMPgg4KCzEmWEEIIIeo3zROgUaNGcf78eWbMmEFycjLh4eFs2LDBXMQcHx+P/rKJt/r06cPSpUt59dVXefnll2nRogWrV682zwEE8MILL5Cbm8u//vUvMjIy6NevHxs2bKjUHEBCCCGEqPs0nwdI2L7CwkJmz57N9OnTr+hKFNqR96V2kveldpL3pXay5vsiCZAQQggh6p3avaiHEEIIIYQVSAIkhBBCiHpHEiAhhBBC1DuSAAkhhBCi3pEESFTa66+/jk6nK3dr3bq1+fGCggImTpxIw4YNcXNz45577rli1m5heYmJiTz88MM0bNgQZ2dnOnTowJ49e8yPK4rCjBkzCAwMxNnZmcGDB3Pq1CkNI677wsLCrvhd0el0TJw4EZDfFa0YjUZee+01mjRpgrOzM82aNWPWrFnl1o2S3xdtZGdnM2XKFEJDQ3F2dqZPnz7s3n1p+ServC+KEJU0c+ZMpV27dkpSUpL5dv78efPjTz75pBISEqJERkYqe/bsUXr16qX06dNHw4jrvvT0dCU0NFQZN26c8tdffylnzpxRNm7cqERHR5uPeeeddxRPT09l9erVysGDB5W77rpLadKkiZKfn69h5HVbampqud+TTZs2KYCydetWRVHkd0Urb731ltKwYUNl7dq1SkxMjLJixQrFzc1N+eCDD8zHyO+LNu6//36lbdu2yvbt25VTp04pM2fOVDw8PJSzZ88qimKd90USIFFpM2fOVDp16lThYxkZGYq9vb2yYsUK877jx48rgBIVFVVDEdY/L774otKvX7+rPm4ymZSAgADlvffeM+/LyMhQHB0dle+//74mQhSKojzzzDNKs2bNFJPJJL8rGho6dKjy6KOPlts3cuRI5aGHHlIURX5ftJKXl6cYDAZl7dq15fZ36dJFeeWVV6z2vkgXmKiSU6dOERQURNOmTXnooYeIj48HYO/evRQXFzN48GDzsa1bt6Zx48ZERUVpFW6d9/PPP9OtWzfuu+8+/Pz86Ny5M5999pn58ZiYGJKTk8u9L56envTs2VPelxpSVFTEd999x6OPPopOp5PfFQ316dOHyMhI/v77bwAOHjzIjh07uP322wH5fdFKSUkJRqPxitUanJ2d2bFjh9XeF0mARKX17NmTxYsXs2HDBhYuXEhMTAz9+/cnOzub5ORkHBwcrlhE1t/fn+TkZG0CrgfOnDnDwoULadGiBRs3bmTChAlMnjyZr7/+GsD8vS9bWqaMvC81Z/Xq1WRkZDBu3DgA+V3R0EsvvcQDDzxA69atsbe3p3PnzkyZMoWHHnoIkN8Xrbi7u9O7d29mzZrFuXPnMBqNfPfdd0RFRZGUlGS190XztcCE7Sj7LwmgY8eO9OzZk9DQUH744QecnZ01jKz+MplMdOvWjbfffhuAzp07c+TIERYtWsTYsWM1jk4AfPHFF9x+++0EBQVpHUq998MPP7BkyRKWLl1Ku3btOHDgAFOmTCEoKEh+XzT27bff8uijjxIcHIzBYKBLly6MHj2avXv3Wu2a0gIkqs3Ly4uWLVsSHR1NQEAARUVFZGRklDsmJSWFgIAAbQKsBwIDA2nbtm25fW3atDF3TZZ97/85wkjel5oRFxfH5s2befzxx8375HdFO9OmTTO3AnXo0IFHHnmEZ599ltmzZwPy+6KlZs2asX37dnJyckhISGDXrl0UFxfTtGlTq70vkgCJasvJyeH06dMEBgbStWtX7O3tiYyMND9+8uRJ4uPj6d27t4ZR1m19+/bl5MmT5fb9/fffhIaGAtCkSRMCAgLKvS9ZWVn89ddf8r7UgK+++go/Pz+GDh1q3ie/K9rJy8tDry//sWcwGDCZTID8vtQGrq6uBAYGcvHiRTZu3Mjw4cOt975Uu3xa1DvPPfecsm3bNiUmJkb5448/lMGDBys+Pj5Kamqqoijq0N7GjRsrW7ZsUfbs2aP07t1b6d27t8ZR1227du1S7OzslLfeeks5deqUsmTJEsXFxUX57rvvzMe88847ipeXl7JmzRrl0KFDyvDhw2VYbw0wGo1K48aNlRdffPGKx+R3RRtjx45VgoODzcPgV61apfj4+CgvvPCC+Rj5fdHGhg0blPXr1ytnzpxRfv31V6VTp05Kz549laKiIkVRrPO+SAIkKm3UqFFKYGCg4uDgoAQHByujRo0qN99Mfn6+8tRTTykNGjRQXFxclLvvvltJSkrSMOL64f/+7/+U9u3bK46Ojkrr1q2VTz/9tNzjJpNJee211xR/f3/F0dFRueWWW5STJ09qFG39sXHjRgWo8HstvyvayMrKUp555hmlcePGipOTk9K0aVPllVdeUQoLC83HyO+LNpYvX640bdpUcXBwUAICApSJEycqGRkZ5set8b7oFOWyKTCFEEIIIeoBqQESQgghRL0jCZAQQggh6h1JgIQQQghR70gCJIQQQoh6RxIgIYQQQtQ7kgAJIYQQot6RBEgIIYQQ9Y4kQEIIIYSodyQBEkLUGVFRURgMhnJrbwkhREVkJmghRJ3x+OOP4+bmxhdffMHJkycJCgrSOiQhRC0lLUBCiDohJyeH5cuXM2HCBIYOHcrixYvLPf7zzz/TokULnJycGDRoEF9//TU6nY6MjAzzMTt27KB///44OzsTEhLC5MmTyc3NrdkXIoSoEZIACSHqhB9++IHWrVvTqlUrHn74Yb788kvKGrhjYmK49957GTFiBAcPHuTf//43r7zySrnnnz59miFDhnDPPfdw6NAhli9fzo4dO5g0aZIWL0cIYWXSBSaEqBP69u3L/fffzzPPPENJSQmBgYGsWLGCgQMH8tJLL/HLL79w+PBh8/Gvvvoqb731FhcvXsTLy4vHH38cg8HA//73P/MxO3bsYMCAAeTm5uLk5KTFyxJCWIm0AAkhbN7JkyfZtWsXo0ePBsDOzo5Ro0bxxRdfmB/v3r17uef06NGj3NcHDx5k8eLFuLm5mW8RERGYTCZiYmJq5oUIIWqMndYBCCHEjfriiy8oKSkpV/SsKAqOjo589NFHlTpHTk4O//73v5k8efIVjzVu3NhisQohagdJgIQQNq2kpIRvvvmGuXPnctttt5V7bMSIEXz//fe0atWKdevWlXts9+7d5b7u0qULx44do3nz5laPWQihPakBEkLYtNWrVzNq1ChSU1Px9PQs99iLL77Ili1b+OGHH2jVqhXPPvssjz32GAcOHOC5557j7NmzZGRk4OnpyaFDh+jVqxePPvoojz/+OK6urhw7doxNmzZVuhVJCGE7pAZICGHTvvjiCwYPHnxF8gNwzz33sGfPHrKzs1m5ciWrVq2iY8eOLFy40DwKzNHREYCOHTuyfft2/v77b/r370/nzp2ZMWOGzCUkRB0lLUBCiHrprbfeYtGiRSQkJGgdihBCA1IDJISoFz755BO6d+9Ow4YN+eOPP3jvvfdkjh8h6jFJgIQQ9cKpU6d48803SU9Pp3Hjxjz33HNMnz5d67CEEBqRLjAhhBBC1DtSBC2EEEKIekcSICGEEELUO5IACSGEEKLekQRICCGEEPWOJEBCCCGEqHckARJCCCFEvSMJkBBCCCHqHUmAhBBCCFHvSAIkhBBCiHrn/wGSnfsbvOXuugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset1 : (133 samples, 335 features)\n",
      "Dataset2 : (232 samples, 175 features)\n"
     ]
    }
   ],
   "source": [
    "sns.kdeplot(\n",
    "    data1,\n",
    "    x='Age',\n",
    "    label='Dataset 1'\n",
    ").set_title(\"Age distribution of datasets\")\n",
    "\n",
    "sns.kdeplot(\n",
    "    data2,\n",
    "    x='Age',\n",
    "    label='Dataset 2'\n",
    ")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(f'Dataset1 : ({data1.shape[0]} samples, {data1.shape[1]} features)')\n",
    "print(f'Dataset2 : ({data2.shape[0]} samples, {data2.shape[1]} features)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4eb3e7b",
   "metadata": {},
   "source": [
    "## Format dataset 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ed420e3",
   "metadata": {
    "id": "1ed420e3"
   },
   "outputs": [],
   "source": [
    "data_train1, data_test1, y_train1, y_test1 = train_test_split(data1.drop(['Age'], axis=1), data1['Age'], test_size=0.3, random_state=2)\n",
    "x_train1, del_col1, mean_train1, std_train1 = helpers.clean_data(1, data_train1)\n",
    "x_test1, _, _, _ = helpers.clean_data(1, data_test1, del_col1, mean_train1, std_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39fd4c9f",
   "metadata": {
    "id": "39fd4c9f"
   },
   "outputs": [],
   "source": [
    "x_train1 = x_train1.reset_index(drop=True)\n",
    "x_test1 = x_test1.reset_index(drop=True)\n",
    "y_train1 = pd.DataFrame(data=y_train1, columns=['Age']).reset_index(drop=True)\n",
    "y_test1 = pd.DataFrame(data=y_test1, columns=['Age']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67af90bd",
   "metadata": {},
   "source": [
    "## Format dataset 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c464f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train2, data_test2, y_train2, y_test2 = train_test_split(data2.drop(['Age'], axis=1), data2['Age'], test_size=0.3, random_state=2)\n",
    "x_train2, del_col2, mean_train2, std_train2 = helpers.clean_data(2, data_train2)\n",
    "x_test2, _, _, _ = helpers.clean_data(2, data_test2, del_col2, mean_train2, std_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "108b6e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train2 = x_train2.reset_index(drop=True)\n",
    "x_test2 = x_test2.reset_index(drop=True)\n",
    "y_train2 = pd.DataFrame(data=y_train2, columns=['Age']).reset_index(drop=True)\n",
    "y_test2 = pd.DataFrame(data=y_test2, columns=['Age']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8806fa41",
   "metadata": {},
   "source": [
    "## Select dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e4cba3d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "i_data = 1\n",
    "\n",
    "if (i_data == 1):\n",
    "    x_train = x_train1\n",
    "    x_test = x_test1\n",
    "    y_train = y_train1\n",
    "    y_test = y_test1\n",
    "elif (i_data == 2):\n",
    "    x_train = x_train2\n",
    "    x_test = x_test2\n",
    "    y_train = y_train2\n",
    "    y_test = y_test2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e290d0",
   "metadata": {
    "id": "89e290d0"
   },
   "source": [
    "### Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "002589ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# Create grids for search parameters\n",
    "param_grid_svm = [\n",
    "    {'clf__C': np.logspace(-3, 3, 10), 'clf__kernel': ['linear']},\n",
    "    {'clf__C': np.logspace(-3, 3, 10), 'clf__gamma': np.logspace(-10, 0, 20), 'clf__kernel': ['rbf']},\n",
    "]\n",
    "\n",
    "param_grid_elastic = {\n",
    "    'clf__alpha': np.logspace(-10,7,25),\n",
    "    'clf__l1_ratio': np.append(np.logspace(-10,0,20), 0)\n",
    "}\n",
    "\n",
    "param_grid_adaboost = {\n",
    "    'clf__n_estimators': np.arange(1, 200, 20),\n",
    "    'clf__learning_rate': [1.0, 0.5, 0.1]\n",
    "}\n",
    "\n",
    "param_grid_forest = {\n",
    "    'clf__n_estimators': np.arange(10, 100, 20),\n",
    "    'clf__max_depth': np.arange(1, 20)\n",
    "}\n",
    "\n",
    "param_grid_knn = {\n",
    "    'clf__n_neighbors': np.arange(1, 20)\n",
    "}\n",
    "\n",
    "\n",
    "# Create models grid search\n",
    "model_svr = helpers.model_gs(\n",
    "    svm.SVR(),\n",
    "    param_grid_svm\n",
    ")\n",
    "\n",
    "model_elastic = helpers.model_gs(\n",
    "    ElasticNet(max_iter=5000),\n",
    "    param_grid_elastic\n",
    ")\n",
    "\n",
    "model_adaboost = helpers.model_gs(\n",
    "    AdaBoostRegressor(random_state = 123),\n",
    "    param_grid_adaboost\n",
    ")\n",
    "\n",
    "model_forest = helpers.model_gs(\n",
    "    RandomForestRegressor(),\n",
    "    param_grid_forest\n",
    ")\n",
    "\n",
    "model_knn = helpers.model_gs(\n",
    "    KNeighborsRegressor(),\n",
    "    param_grid_knn\n",
    ")\n",
    "\n",
    "# Create models list\n",
    "models = {\n",
    "    \"SVR\": model_svr,\n",
    "    \"ElasticNet\": model_elastic,\n",
    "    \"AdaBoost\": model_adaboost,\n",
    "    \"RandomForest\": model_forest,\n",
    "    \"KNN\": model_knn\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a122fc28",
   "metadata": {
    "id": "a122fc28"
   },
   "source": [
    "# PLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "78af5de4",
   "metadata": {
    "id": "78af5de4"
   },
   "outputs": [],
   "source": [
    "# PLS Regression\n",
    "pls_train, pls_test = helpers.PLS_regression(x_train, x_test, y_train, y_test, n_components = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c12033",
   "metadata": {
    "id": "10c12033"
   },
   "source": [
    "### Standardize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "aa167953",
   "metadata": {
    "id": "aa167953"
   },
   "outputs": [],
   "source": [
    "pls_train_scaled, pls_test_scaled = helpers.standardize(pls_train, pls_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2bea9d",
   "metadata": {},
   "source": [
    "### Filter MI Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "77655acd",
   "metadata": {
    "id": "77655acd"
   },
   "outputs": [],
   "source": [
    "pls_train_scaled, pls_test_scaled = helpers.filter_mi_scores(pls_train_scaled, pls_test_scaled, y_train, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83cbee66",
   "metadata": {
    "id": "83cbee66"
   },
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0b8c9e79",
   "metadata": {
    "id": "0b8c9e79"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "pls_train_scaled, pls_test_scaled = helpers.KM_clustering(pls_train_scaled, pls_test_scaled, n_clusters=3)\n",
    "#pls_train_scaled, pls_test_scaled = helpers.GMM_clustering(pls_train_scaled, pls_test_scaled, n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f9a6246e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n",
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "\u001b[1m -- Global Model -- \u001b[0m\n",
      "Best R2 : AdaBoostRegressor(n_estimators=181, random_state=123)\n",
      "Best MAE : KNeighborsRegressor(n_neighbors=4)\n",
      "\u001b[1m                         Model Train R2 Test R2 Train MAE Test MAE\n",
      "Best R2     AdaBoostRegressor    0.931   0.075     0.734    2.782\n",
      "Best MAE  KNeighborsRegressor    0.731   -0.03     1.346    2.737 \u001b[0m\n",
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n",
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n",
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n",
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "-- Global Model on clusters\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.909993  0.272263   0.794121  2.294765\n",
      "Best R2 - Cluster 1  0.826587  0.091546   0.806729  3.122220\n",
      "Best R2 - Cluster 2  0.972514 -0.423084   0.555070  2.841658\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.699894  0.241492   1.284722  2.145833\n",
      "Best MAE - Cluster 1  0.630727  0.067546   1.140625  2.966667\n",
      "Best MAE - Cluster 2  0.741822 -0.875826   1.700000  3.019231\n",
      "-- Clusters Models --\n",
      "Best r2 - Cluster 0 : KNeighborsRegressor(n_neighbors=4)\n",
      "Best r2 - Cluster 1 : RandomForestRegressor(max_depth=7, n_estimators=30)\n",
      "Best r2 - Cluster 2 : AdaBoostRegressor(n_estimators=161, random_state=123)\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.643237  0.509926   1.548611  1.833333\n",
      "Best R2 - Cluster 1  0.909867 -0.085773   0.559375  3.306667\n",
      "Best R2 - Cluster 2  0.993763 -0.318243   0.229264  2.573581\n",
      "Best mae - Cluster 0 : KNeighborsRegressor(n_neighbors=4)\n",
      "Best mae - Cluster 1 : KNeighborsRegressor(n_neighbors=2)\n",
      "Best mae - Cluster 2 : AdaBoostRegressor(n_estimators=161, random_state=123)\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.643237  0.509926   1.548611  1.833333\n",
      "Best MAE - Cluster 1  0.775166 -0.160522   0.859375  3.133333\n",
      "Best MAE - Cluster 2  0.993763 -0.318243   0.229264  2.573581\n",
      "-- Best Models --\n",
      "Best r2 - Cluster 0 : KNeighborsRegressor(n_neighbors=4)\n",
      "Best r2 - Cluster 1 : AdaBoostRegressor(n_estimators=181, random_state=123)\n",
      "Best r2 - Cluster 2 : AdaBoostRegressor(n_estimators=161, random_state=123)\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.643237  0.509926   1.548611  1.833333\n",
      "Best R2 - Cluster 1  0.826587  0.091546   0.806729  3.122220\n",
      "Best R2 - Cluster 2  0.993763 -0.318243   0.229264  2.573581\n",
      "Best mae - Cluster 0 : KNeighborsRegressor(n_neighbors=4)\n",
      "Best mae - Cluster 1 : KNeighborsRegressor(n_neighbors=4)\n",
      "Best mae - Cluster 2 : AdaBoostRegressor(n_estimators=161, random_state=123)\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.643237  0.509926   1.548611  1.833333\n",
      "Best MAE - Cluster 1  0.630727  0.067546   1.140625  2.966667\n",
      "Best MAE - Cluster 2  0.993763 -0.318243   0.229264  2.573581\n",
      "-- Optimization results --\n",
      "Best r2 - Cluster 0 : local\n",
      "Best r2 - Cluster 1 : global\n",
      "Best r2 - Cluster 2 : local\n",
      "Best mae - Cluster 0 : local\n",
      "Best mae - Cluster 1 : global\n",
      "Best mae - Cluster 2 : local\n",
      "\u001b[1m           Train R2   Test R2  Train MAE  Test MAE\n",
      "Best r2   0.857657  0.163649   0.938677  2.557246\n",
      "Best mae  0.825864  0.152016   1.053566  2.498914 \u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [ 0.03372429  0.38209605  0.4480113   0.40858641  0.39507163  0.34180965\n",
      "  0.43054148  0.41618969  0.37188839  0.35022791  0.26758024  0.18545403\n",
      "  0.10530986  0.05117152 -0.02921968 -0.13324941         nan         nan\n",
      "         nan]\n",
      "  warnings.warn(\n",
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the train scores are non-finite: [1.         0.84096681 0.72445051 0.68016054 0.64665853 0.57398734\n",
      " 0.54013511 0.49285571 0.45398938 0.39566882 0.34840532 0.2797253\n",
      " 0.21999931 0.15969918 0.10092376 0.04542409        nan        nan\n",
      "        nan]\n",
      "  warnings.warn(\n",
      "C:\\Users\\anton\\Documents\\MA1\\project2_brainAge\\helpers.py:653: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results = results.append(\n",
      "C:\\Users\\anton\\Documents\\MA1\\project2_brainAge\\helpers.py:653: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results = results.append(\n"
     ]
    }
   ],
   "source": [
    "results_pls_optimized, results_pls_global, pls_best_models = helpers.optimize_clusters(models, pls_train_scaled, pls_test_scaled, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f36fc8a6",
   "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>Model</th>\n",
       "      <th>Train R2</th>\n",
       "      <th>Test R2</th>\n",
       "      <th>Train MAE</th>\n",
       "      <th>Test MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best R2</th>\n",
       "      <td>AdaBoostRegressor</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.734</td>\n",
       "      <td>2.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best MAE</th>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.731</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.346</td>\n",
       "      <td>2.737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model Train R2 Test R2 Train MAE Test MAE\n",
       "Best R2     AdaBoostRegressor    0.931   0.075     0.734    2.782\n",
       "Best MAE  KNeighborsRegressor    0.731   -0.03     1.346    2.737"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_pls_global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e9cfe6c2",
   "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>Train R2</th>\n",
       "      <th>Test R2</th>\n",
       "      <th>Train MAE</th>\n",
       "      <th>Test MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best r2</th>\n",
       "      <td>0.857657</td>\n",
       "      <td>0.163649</td>\n",
       "      <td>0.938677</td>\n",
       "      <td>2.557246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best mae</th>\n",
       "      <td>0.825864</td>\n",
       "      <td>0.152016</td>\n",
       "      <td>1.053566</td>\n",
       "      <td>2.498914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Train R2   Test R2  Train MAE  Test MAE\n",
       "Best r2   0.857657  0.163649   0.938677  2.557246\n",
       "Best mae  0.825864  0.152016   1.053566  2.498914"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_pls_optimized"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab0295f",
   "metadata": {
    "id": "8ab0295f"
   },
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0a1c0ac9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "id": "0a1c0ac9",
    "outputId": "9ce73e8b-be62-48cf-e64d-e8ef56b641d4"
   },
   "outputs": [],
   "source": [
    "pc_train, pc_test = helpers.PCA_decomposition(x_train, x_test, y_train, y_test, n_components=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e87996",
   "metadata": {
    "id": "97e87996"
   },
   "source": [
    "### Standardize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "917b8131",
   "metadata": {
    "id": "917b8131"
   },
   "outputs": [],
   "source": [
    "pc_train_scaled, pc_test_scaled = helpers.standardize(pc_train, pc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eb3bd84",
   "metadata": {
    "id": "3eb3bd84"
   },
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f75a8edf",
   "metadata": {
    "id": "f75a8edf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "pc_train_scaled, pc_test_scaled = helpers.KM_clustering(pc_train_scaled, pc_test_scaled, n_clusters=3)\n",
    "#pc_train_scaled, pc_test_scaled = helpers.GMM_clustering(pc_train_scaled, pc_test_scaled, n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "946b7253",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:634: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.919e+02, tolerance: 1.130e-01 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "\u001b[1m -- Global Model -- \u001b[0m\n",
      "Best R2 : ElasticNet(alpha=0.8254041852680207, l1_ratio=0.0, max_iter=5000)\n",
      "Best MAE : ElasticNet(alpha=0.8254041852680207, l1_ratio=0.0, max_iter=5000)\n",
      "\u001b[1m                Model Train R2 Test R2 Train MAE Test MAE\n",
      "Best R2   ElasticNet    0.445   0.209     1.972    2.649\n",
      "Best MAE  ElasticNet    0.445   0.209     1.972    2.649 \u001b[0m\n",
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:634: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.412e+02, tolerance: 2.830e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [-4.18356198 -2.19986197 -1.4592754  -1.33033023 -1.14918656 -1.06720191\n",
      " -1.01901421 -0.98394147 -0.9225893  -0.89374583 -0.85509289 -0.87419892\n",
      " -0.96612428         nan         nan         nan         nan         nan\n",
      "         nan]\n",
      "  warnings.warn(\n",
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the train scores are non-finite: [ 1.00000000e+00  3.55137071e-01  1.74758411e-01  9.38724067e-02\n",
      "  8.42201039e-02  7.19289610e-02  6.27603684e-02  5.22080713e-02\n",
      "  4.07841262e-02  3.18681108e-02  1.67428531e-02  5.03657518e-03\n",
      " -8.51790935e-04             nan             nan             nan\n",
      "             nan             nan             nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n",
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:634: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.242e+02, tolerance: 4.490e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "-- Global Model on clusters\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.298221  0.375008   2.279864  2.067731\n",
      "Best R2 - Cluster 1  0.465010  0.205236   1.452695  2.873491\n",
      "Best R2 - Cluster 2  0.367323  0.077970   2.776906  2.701287\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.298221  0.375008   2.279864  2.067731\n",
      "Best MAE - Cluster 1  0.465010  0.205236   1.452695  2.873491\n",
      "Best MAE - Cluster 2  0.367323  0.077970   2.776906  2.701287\n",
      "-- Clusters Models --\n",
      "Best r2 - Cluster 0 : AdaBoostRegressor(learning_rate=0.5, n_estimators=81, random_state=123)\n",
      "Best r2 - Cluster 1 : ElasticNet(alpha=0.16155980984398763, l1_ratio=0.2976351441631313,\n",
      "           max_iter=5000)\n",
      "Best r2 - Cluster 2 : KNeighborsRegressor(n_neighbors=10)\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.985050  0.275030   0.323106  2.312654\n",
      "Best R2 - Cluster 1  0.690524  0.001151   1.071522  3.036614\n",
      "Best R2 - Cluster 2  0.006923 -0.098137   3.337500  2.870000\n",
      "Best mae - Cluster 0 : AdaBoostRegressor(learning_rate=0.5, n_estimators=81, random_state=123)\n",
      "Best mae - Cluster 1 : ElasticNet(alpha=0.16155980984398763, l1_ratio=0.2976351441631313,\n",
      "           max_iter=5000)\n",
      "Best mae - Cluster 2 : KNeighborsRegressor(n_neighbors=10)\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.985050  0.275030   0.323106  2.312654\n",
      "Best MAE - Cluster 1  0.690524  0.001151   1.071522  3.036614\n",
      "Best MAE - Cluster 2  0.006923 -0.098137   3.337500  2.870000\n",
      "-- Best Models --\n",
      "Best r2 - Cluster 0 : ElasticNet(alpha=0.8254041852680207, l1_ratio=0.0, max_iter=5000)\n",
      "Best r2 - Cluster 1 : ElasticNet(alpha=0.8254041852680207, l1_ratio=0.0, max_iter=5000)\n",
      "Best r2 - Cluster 2 : ElasticNet(alpha=0.8254041852680207, l1_ratio=0.0, max_iter=5000)\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.298221  0.375008   2.279864  2.067731\n",
      "Best R2 - Cluster 1  0.465010  0.205236   1.452695  2.873491\n",
      "Best R2 - Cluster 2  0.367323  0.077970   2.776906  2.701287\n",
      "Best mae - Cluster 0 : ElasticNet(alpha=0.8254041852680207, l1_ratio=0.0, max_iter=5000)\n",
      "Best mae - Cluster 1 : ElasticNet(alpha=0.8254041852680207, l1_ratio=0.0, max_iter=5000)\n",
      "Best mae - Cluster 2 : ElasticNet(alpha=0.8254041852680207, l1_ratio=0.0, max_iter=5000)\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.298221  0.375008   2.279864  2.067731\n",
      "Best MAE - Cluster 1  0.465010  0.205236   1.452695  2.873491\n",
      "Best MAE - Cluster 2  0.367323  0.077970   2.776906  2.701287\n",
      "-- Optimization results --\n",
      "Best r2 - Cluster 0 : global\n",
      "Best r2 - Cluster 1 : global\n",
      "Best r2 - Cluster 2 : global\n",
      "Best mae - Cluster 0 : global\n",
      "Best mae - Cluster 1 : global\n",
      "Best mae - Cluster 2 : global\n",
      "\u001b[1m           Train R2   Test R2  Train MAE  Test MAE\n",
      "Best r2   0.445216  0.209084   1.972313  2.649144\n",
      "Best mae  0.445216  0.209084   1.972313  2.649144 \u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [-0.73643282 -0.10248603 -0.14769913 -0.12531273 -0.14196791 -0.20223969\n",
      " -0.17063601 -0.12759926 -0.09291834 -0.09006575 -0.11549126 -0.09087354\n",
      " -0.1053891  -0.13614995 -0.14635776 -0.14477182         nan         nan\n",
      "         nan]\n",
      "  warnings.warn(\n",
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the train scores are non-finite: [ 1.          0.68208576  0.46857262  0.35971418  0.223885    0.12107966\n",
      "  0.08486585  0.06976499  0.08098876  0.06292283  0.03530883  0.02348713\n",
      " -0.0067715  -0.00680771 -0.00517988  0.                 nan         nan\n",
      "         nan]\n",
      "  warnings.warn(\n",
      "C:\\Users\\anton\\Documents\\MA1\\project2_brainAge\\helpers.py:653: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results = results.append(\n",
      "C:\\Users\\anton\\Documents\\MA1\\project2_brainAge\\helpers.py:653: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results = results.append(\n"
     ]
    }
   ],
   "source": [
    "results_pca_optimized, results_pca_global, pca_best_models = helpers.optimize_clusters(models, pc_train_scaled, pc_test_scaled, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "219c4b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Model</th>\n",
       "      <th>Train R2</th>\n",
       "      <th>Test R2</th>\n",
       "      <th>Train MAE</th>\n",
       "      <th>Test MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best R2</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.209</td>\n",
       "      <td>1.972</td>\n",
       "      <td>2.649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best MAE</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.209</td>\n",
       "      <td>1.972</td>\n",
       "      <td>2.649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model Train R2 Test R2 Train MAE Test MAE\n",
       "Best R2   ElasticNet    0.445   0.209     1.972    2.649\n",
       "Best MAE  ElasticNet    0.445   0.209     1.972    2.649"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_pca_global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2c8df62f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Train R2</th>\n",
       "      <th>Test R2</th>\n",
       "      <th>Train MAE</th>\n",
       "      <th>Test MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best r2</th>\n",
       "      <td>0.445216</td>\n",
       "      <td>0.209084</td>\n",
       "      <td>1.972313</td>\n",
       "      <td>2.649144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best mae</th>\n",
       "      <td>0.445216</td>\n",
       "      <td>0.209084</td>\n",
       "      <td>1.972313</td>\n",
       "      <td>2.649144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Train R2   Test R2  Train MAE  Test MAE\n",
       "Best r2   0.445216  0.209084   1.972313  2.649144\n",
       "Best mae  0.445216  0.209084   1.972313  2.649144"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_pca_optimized"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd9665d",
   "metadata": {
    "id": "69fIn12qwZv7"
   },
   "source": [
    "# Raw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2de88161",
   "metadata": {},
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cd026359",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "raw_train_scaled, raw_test_scaled = helpers.KM_clustering(x_train, x_test, n_clusters=3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "233aa3ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n",
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "\u001b[1m -- Global Model -- \u001b[0m\n",
      "Best R2 : RandomForestRegressor(max_depth=17, n_estimators=30)\n",
      "Best MAE : KNeighborsRegressor(n_neighbors=6)\n",
      "\u001b[1m                           Model Train R2 Test R2 Train MAE Test MAE\n",
      "Best R2   RandomForestRegressor    0.891   0.102     0.915    2.895\n",
      "Best MAE    KNeighborsRegressor    0.372   0.033     2.019    2.870 \u001b[0m\n",
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:634: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.391e+01, tolerance: 4.281e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n",
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [-1.54302881 -0.57990449 -0.36663824 -0.24404079 -0.07183804 -0.10314303\n",
      " -0.41618374 -1.06105707         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan]\n",
      "  warnings.warn(\n",
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the train scores are non-finite: [1.         0.67672662 0.5854747  0.49909963 0.3866256  0.26178635\n",
      " 0.16281871 0.                nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n",
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "-- Global Model on clusters\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.877967  0.010520   0.896825  2.948148\n",
      "Best R2 - Cluster 1  0.901464  0.077914   0.986111  3.127778\n",
      "Best R2 - Cluster 2  0.898148  0.043420   0.914530  2.750000\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.283818  0.139222   1.896825  2.546296\n",
      "Best MAE - Cluster 1  0.499461 -0.617402   1.805556  3.944444\n",
      "Best MAE - Cluster 2  0.389146  0.033333   2.217949  2.833333\n",
      "-- Clusters Models --\n",
      "Best r2 - Cluster 0 : AdaBoostRegressor(n_estimators=141, random_state=123)\n",
      "Best r2 - Cluster 1 : ElasticNet(alpha=0.03162277660168379, l1_ratio=0.2976351441631313,\n",
      "           max_iter=5000)\n",
      "Best r2 - Cluster 2 : SVR(C=1000.0, gamma=1.8329807108324338e-05)\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.979831  0.234916   0.322824  2.294654\n",
      "Best R2 - Cluster 1  0.999970  0.048306   0.016913  2.769921\n",
      "Best R2 - Cluster 2  0.974576  0.184956   0.219302  2.647189\n",
      "Best mae - Cluster 0 : AdaBoostRegressor(n_estimators=141, random_state=123)\n",
      "Best mae - Cluster 1 : ElasticNet(alpha=0.03162277660168379, l1_ratio=0.2976351441631313,\n",
      "           max_iter=5000)\n",
      "Best mae - Cluster 2 : ElasticNet(alpha=0.00618965818891261, l1_ratio=0.026366508987303555,\n",
      "           max_iter=5000)\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.979831  0.234916   0.322824  2.294654\n",
      "Best MAE - Cluster 1  0.999970  0.048306   0.016913  2.769921\n",
      "Best MAE - Cluster 2  0.999998  0.160545   0.004328  2.626399\n",
      "-- Best Models --\n",
      "Best r2 - Cluster 0 : AdaBoostRegressor(n_estimators=141, random_state=123)\n",
      "Best r2 - Cluster 1 : RandomForestRegressor(max_depth=17, n_estimators=30)\n",
      "Best r2 - Cluster 2 : SVR(C=1000.0, gamma=1.8329807108324338e-05)\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.979831  0.234916   0.322824  2.294654\n",
      "Best R2 - Cluster 1  0.901464  0.077914   0.986111  3.127778\n",
      "Best R2 - Cluster 2  0.974576  0.184956   0.219302  2.647189\n",
      "Best mae - Cluster 0 : AdaBoostRegressor(n_estimators=141, random_state=123)\n",
      "Best mae - Cluster 1 : ElasticNet(alpha=0.03162277660168379, l1_ratio=0.2976351441631313,\n",
      "           max_iter=5000)\n",
      "Best mae - Cluster 2 : ElasticNet(alpha=0.00618965818891261, l1_ratio=0.026366508987303555,\n",
      "           max_iter=5000)\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.979831  0.234916   0.322824  2.294654\n",
      "Best MAE - Cluster 1  0.999970  0.048306   0.016913  2.769921\n",
      "Best MAE - Cluster 2  0.999998  0.160545   0.004328  2.626399\n",
      "-- Optimization results --\n",
      "Best r2 - Cluster 0 : local\n",
      "Best r2 - Cluster 1 : global\n",
      "Best r2 - Cluster 2 : local\n",
      "Best mae - Cluster 0 : local\n",
      "Best mae - Cluster 1 : local\n",
      "Best mae - Cluster 2 : local\n",
      "\u001b[1m           Train R2   Test R2  Train MAE  Test MAE\n",
      "Best r2   0.965133  0.244188   0.364997  2.560637\n",
      "Best mae  0.992355  0.230560   0.149789  2.498642 \u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anton\\Documents\\MA1\\project2_brainAge\\helpers.py:653: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results = results.append(\n",
      "C:\\Users\\anton\\Documents\\MA1\\project2_brainAge\\helpers.py:653: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results = results.append(\n"
     ]
    }
   ],
   "source": [
    "results_raw_optimized, results_raw_global, raw_best_models = helpers.optimize_clusters(models, raw_train_scaled, raw_test_scaled, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7a4b4831",
   "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>Model</th>\n",
       "      <th>Train R2</th>\n",
       "      <th>Test R2</th>\n",
       "      <th>Train MAE</th>\n",
       "      <th>Test MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best R2</th>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.915</td>\n",
       "      <td>2.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best MAE</th>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.033</td>\n",
       "      <td>2.019</td>\n",
       "      <td>2.870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Model Train R2 Test R2 Train MAE Test MAE\n",
       "Best R2   RandomForestRegressor    0.891   0.102     0.915    2.895\n",
       "Best MAE    KNeighborsRegressor    0.372   0.033     2.019    2.870"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_raw_global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d6fa4a9d",
   "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>Train R2</th>\n",
       "      <th>Test R2</th>\n",
       "      <th>Train MAE</th>\n",
       "      <th>Test MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best r2</th>\n",
       "      <td>0.965133</td>\n",
       "      <td>0.244188</td>\n",
       "      <td>0.364997</td>\n",
       "      <td>2.560637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best mae</th>\n",
       "      <td>0.992355</td>\n",
       "      <td>0.230560</td>\n",
       "      <td>0.149789</td>\n",
       "      <td>2.498642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Train R2   Test R2  Train MAE  Test MAE\n",
       "Best r2   0.965133  0.244188   0.364997  2.560637\n",
       "Best mae  0.992355  0.230560   0.149789  2.498642"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_raw_optimized"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714606da",
   "metadata": {},
   "source": [
    "# Lasso Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f4099e6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:1561: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso picked 40 variables and eliminated the other 255 variables\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:634: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.581e-01, tolerance: 1.130e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "# data1 : thr=0.2, data2 : thr=0\n",
    "lasso_train, lasso_test = helpers.filter_lasso(x_train, x_test, y_train, threshold=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e1622fc",
   "metadata": {},
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "41b4a490",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "lasso_train, lasso_test = helpers.KM_clustering(lasso_train, lasso_test, n_clusters=3)\n",
    "#lasso_train, lasso_test = helpers.GMM_clustering(lasso_train, lasso_test, n_clusters=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3f06c694",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:634: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.719e+01, tolerance: 1.130e-01 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "\u001b[1m -- Global Model -- \u001b[0m\n",
      "Best R2 : KNeighborsRegressor()\n",
      "Best MAE : KNeighborsRegressor()\n",
      "\u001b[1m                         Model Train R2 Test R2 Train MAE Test MAE\n",
      "Best R2   KNeighborsRegressor    0.587   0.054     1.720    2.839\n",
      "Best MAE  KNeighborsRegressor    0.587   0.054     1.720    2.839 \u001b[0m\n",
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:634: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.044e+01, tolerance: 4.759e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:634: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.963e+00, tolerance: 2.814e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [-0.53425092 -0.33815425 -0.29379671 -0.2907475  -0.26451445 -0.26343982\n",
      " -0.19910154 -0.18041574 -0.15849146 -0.10240941 -0.08229344 -0.07960118\n",
      " -0.06773025 -0.08423468 -0.10912905 -0.18735183         nan         nan\n",
      "         nan]\n",
      "  warnings.warn(\n",
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the train scores are non-finite: [1.         0.6816991  0.47924993 0.36737461 0.27897202 0.19846981\n",
      " 0.14085553 0.12944885 0.11425149 0.09983761 0.08769679 0.0823501\n",
      " 0.08114656 0.08090493 0.06215164 0.02960096        nan        nan\n",
      "        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 210 candidates, totalling 3150 fits\n",
      "Fitting 15 folds for each of 525 candidates, totalling 7875 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anton\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:634: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.241e+01, tolerance: 3.592e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 30 candidates, totalling 450 fits\n",
      "Fitting 15 folds for each of 95 candidates, totalling 1425 fits\n",
      "Fitting 15 folds for each of 19 candidates, totalling 285 fits\n",
      "-- Global Model on clusters\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.612856 -0.054951   1.673684  2.647059\n",
      "Best R2 - Cluster 1  0.608900  0.364290   1.576000  2.654545\n",
      "Best R2 - Cluster 2  0.520379 -0.558090   1.900000  3.283333\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.612856 -0.054951   1.673684  2.647059\n",
      "Best MAE - Cluster 1  0.608900  0.364290   1.576000  2.654545\n",
      "Best MAE - Cluster 2  0.520379 -0.558090   1.900000  3.283333\n",
      "-- Clusters Models --\n",
      "Best r2 - Cluster 0 : RandomForestRegressor(max_depth=19, n_estimators=50)\n",
      "Best r2 - Cluster 1 : ElasticNet(alpha=0.03162277660168379, l1_ratio=0.0, max_iter=5000)\n",
      "Best r2 - Cluster 2 : RandomForestRegressor(max_depth=18, n_estimators=30)\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.890725 -0.092078   0.932105  2.961176\n",
      "Best R2 - Cluster 1  0.998387  0.665918   0.110964  1.995938\n",
      "Best R2 - Cluster 2  0.843083 -0.002564   1.040000  2.561111\n",
      "Best mae - Cluster 0 : AdaBoostRegressor(n_estimators=41, random_state=123)\n",
      "Best mae - Cluster 1 : ElasticNet(alpha=0.03162277660168379, l1_ratio=0.0, max_iter=5000)\n",
      "Best mae - Cluster 2 : RandomForestRegressor(max_depth=18, n_estimators=30)\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.965223 -0.139216   0.446438  2.777251\n",
      "Best MAE - Cluster 1  0.998387  0.665918   0.110964  1.995938\n",
      "Best MAE - Cluster 2  0.843083 -0.002564   1.040000  2.561111\n",
      "-- Best Models --\n",
      "Best r2 - Cluster 0 : KNeighborsRegressor()\n",
      "Best r2 - Cluster 1 : ElasticNet(alpha=0.03162277660168379, l1_ratio=0.0, max_iter=5000)\n",
      "Best r2 - Cluster 2 : RandomForestRegressor(max_depth=18, n_estimators=30)\n",
      "                     Train R2   Test R2  Train MAE  Test MAE\n",
      "Best R2 - Cluster 0  0.612856 -0.054951   1.673684  2.647059\n",
      "Best R2 - Cluster 1  0.998387  0.665918   0.110964  1.995938\n",
      "Best R2 - Cluster 2  0.843083 -0.002564   1.040000  2.561111\n",
      "Best mae - Cluster 0 : KNeighborsRegressor()\n",
      "Best mae - Cluster 1 : ElasticNet(alpha=0.03162277660168379, l1_ratio=0.0, max_iter=5000)\n",
      "Best mae - Cluster 2 : RandomForestRegressor(max_depth=18, n_estimators=30)\n",
      "                      Train R2   Test R2  Train MAE  Test MAE\n",
      "Best MAE - Cluster 0  0.612856 -0.054951   1.673684  2.647059\n",
      "Best MAE - Cluster 1  0.998387  0.665918   0.110964  1.995938\n",
      "Best MAE - Cluster 2  0.843083 -0.002564   1.040000  2.561111\n",
      "-- Optimization results --\n",
      "Best r2 - Cluster 0 : global\n",
      "Best r2 - Cluster 1 : local\n",
      "Best r2 - Cluster 2 : local\n",
      "Best mae - Cluster 0 : global\n",
      "Best mae - Cluster 1 : local\n",
      "Best mae - Cluster 2 : local\n",
      "\u001b[1m           Train R2   Test R2  Train MAE  Test MAE\n",
      "Best r2   0.786727  0.293221   1.049184  2.442216\n",
      "Best mae  0.786727  0.293221   1.049184  2.442216 \u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anton\\Documents\\MA1\\project2_brainAge\\helpers.py:653: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results = results.append(\n",
      "C:\\Users\\anton\\Documents\\MA1\\project2_brainAge\\helpers.py:653: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results = results.append(\n"
     ]
    }
   ],
   "source": [
    "results_lasso_optimized, results_lasso_global, lasso_best_models = helpers.optimize_clusters(models, lasso_train, lasso_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "509553de",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train R2</th>\n",
       "      <th>Test R2</th>\n",
       "      <th>Train MAE</th>\n",
       "      <th>Test MAE</th>\n",
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       "      <th>Best R2</th>\n",
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       "      <td>0.587</td>\n",
       "      <td>0.054</td>\n",
       "      <td>1.720</td>\n",
       "      <td>2.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best MAE</th>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.054</td>\n",
       "      <td>1.720</td>\n",
       "      <td>2.839</td>\n",
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       "                        Model Train R2 Test R2 Train MAE Test MAE\n",
       "Best R2   KNeighborsRegressor    0.587   0.054     1.720    2.839\n",
       "Best MAE  KNeighborsRegressor    0.587   0.054     1.720    2.839"
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  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e258c6b1",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best r2</th>\n",
       "      <td>0.786727</td>\n",
       "      <td>0.293221</td>\n",
       "      <td>1.049184</td>\n",
       "      <td>2.442216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best mae</th>\n",
       "      <td>0.786727</td>\n",
       "      <td>0.293221</td>\n",
       "      <td>1.049184</td>\n",
       "      <td>2.442216</td>\n",
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       "          Train R2   Test R2  Train MAE  Test MAE\n",
       "Best r2   0.786727  0.293221   1.049184  2.442216\n",
       "Best mae  0.786727  0.293221   1.049184  2.442216"
      ]
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     "execution_count": 67,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": null,
   "id": "abaf8cc5",
   "metadata": {},
   "outputs": [],
   "source": []
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