3D Heat Transfer RF regression - 1000-12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_excel(r\"Clean3d_master_dataset.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "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>Time (s)</th>\n",
       "      <th>Material</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>Part</th>\n",
       "      <th>Node</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>T_left</th>\n",
       "      <th>T_right</th>\n",
       "      <th>T_bottom</th>\n",
       "      <th>T_top</th>\n",
       "      <th>T_back</th>\n",
       "      <th>T_front</th>\n",
       "      <th>x/L</th>\n",
       "      <th>y/L</th>\n",
       "      <th>z/L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time (s) Material     Alpha      Part  Node     X      Y     Z  \\\n",
       "0      0.01   Copper  0.000111  PART-1-2     1  0.02  0.010  0.01   \n",
       "1      0.01   Copper  0.000111  PART-1-2     2  0.02  0.008  0.01   \n",
       "2      0.01   Copper  0.000111  PART-1-2     3  0.02  0.006  0.01   \n",
       "3      0.01   Copper  0.000111  PART-1-2     4  0.02  0.004  0.01   \n",
       "4      0.01   Copper  0.000111  PART-1-2     5  0.02  0.002  0.01   \n",
       "\n",
       "   Temperature  T_left  T_right  T_bottom  T_top  T_back  T_front  x/L  y/L  \\\n",
       "0        100.0     300      100       300    100     300      100  1.0  1.0   \n",
       "1        100.0     300      100       300    100     300      100  1.0  0.8   \n",
       "2        100.0     300      100       300    100     300      100  1.0  0.6   \n",
       "3        100.0     300      100       300    100     300      100  1.0  0.4   \n",
       "4        100.0     300      100       300    100     300      100  1.0  0.2   \n",
       "\n",
       "   z/L  \n",
       "0  1.0  \n",
       "1  1.0  \n",
       "2  1.0  \n",
       "3  1.0  \n",
       "4  1.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "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>Time (s)</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>Node</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>T_left</th>\n",
       "      <th>T_right</th>\n",
       "      <th>T_bottom</th>\n",
       "      <th>T_top</th>\n",
       "      <th>T_back</th>\n",
       "      <th>T_front</th>\n",
       "      <th>x/L</th>\n",
       "      <th>y/L</th>\n",
       "      <th>z/L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>3.103920e+05</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "      <td>310392.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.756263</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>198.500000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>2.123130e+02</td>\n",
       "      <td>450.034795</td>\n",
       "      <td>100.069589</td>\n",
       "      <td>400.069589</td>\n",
       "      <td>100.139179</td>\n",
       "      <td>333.263744</td>\n",
       "      <td>133.402923</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.763122</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>123.401644</td>\n",
       "      <td>0.006831</td>\n",
       "      <td>0.003416</td>\n",
       "      <td>0.003416</td>\n",
       "      <td>1.469663e+02</td>\n",
       "      <td>111.896899</td>\n",
       "      <td>81.607134</td>\n",
       "      <td>81.692364</td>\n",
       "      <td>81.649671</td>\n",
       "      <td>47.115867</td>\n",
       "      <td>47.165074</td>\n",
       "      <td>0.341566</td>\n",
       "      <td>0.341566</td>\n",
       "      <td>0.341566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.757920e-35</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>90.750000</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>198.500000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>306.250000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>6.000000e+02</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time (s)          Alpha           Node              X  \\\n",
       "count  310392.000000  310392.000000  310392.000000  310392.000000   \n",
       "mean        0.756263       0.000050     198.500000       0.010000   \n",
       "std         1.763122       0.000041     123.401644       0.006831   \n",
       "min         0.010000       0.000004       1.000000       0.000000   \n",
       "25%         0.050000       0.000004      90.750000       0.004000   \n",
       "50%         0.120000       0.000064     198.500000       0.010000   \n",
       "75%         0.500000       0.000111     306.250000       0.016000   \n",
       "max        10.000000       0.000111     396.000000       0.020000   \n",
       "\n",
       "                   Y              Z   Temperature         T_left  \\\n",
       "count  310392.000000  310392.000000  3.103920e+05  310392.000000   \n",
       "mean        0.005000       0.005000  2.123130e+02     450.034795   \n",
       "std         0.003416       0.003416  1.469663e+02     111.896899   \n",
       "min         0.000000       0.000000 -1.757920e-35     300.000000   \n",
       "25%         0.002000       0.002000  1.000000e+02     300.000000   \n",
       "50%         0.005000       0.005000  2.000000e+02     500.000000   \n",
       "75%         0.008000       0.008000  3.000000e+02     600.000000   \n",
       "max         0.010000       0.010000  6.000000e+02     600.000000   \n",
       "\n",
       "             T_right       T_bottom          T_top         T_back  \\\n",
       "count  310392.000000  310392.000000  310392.000000  310392.000000   \n",
       "mean      100.069589     400.069589     100.139179     333.263744   \n",
       "std        81.607134      81.692364      81.649671      47.115867   \n",
       "min         0.000000     300.000000       0.000000     300.000000   \n",
       "25%         0.000000     300.000000       0.000000     300.000000   \n",
       "50%       100.000000     400.000000     100.000000     300.000000   \n",
       "75%       200.000000     500.000000     200.000000     400.000000   \n",
       "max       200.000000     500.000000     200.000000     400.000000   \n",
       "\n",
       "             T_front            x/L            y/L            z/L  \n",
       "count  310392.000000  310392.000000  310392.000000  310392.000000  \n",
       "mean      133.402923       0.500000       0.500000       0.500000  \n",
       "std        47.165074       0.341566       0.341566       0.341566  \n",
       "min       100.000000       0.000000       0.000000       0.000000  \n",
       "25%       100.000000       0.200000       0.200000       0.200000  \n",
       "50%       100.000000       0.500000       0.500000       0.500000  \n",
       "75%       200.000000       0.800000       0.800000       0.800000  \n",
       "max       200.000000       1.000000       1.000000       1.000000  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310392, 18)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = data[ data['x/L'] == 0.1].index\n",
    "newdata = data.drop(indices, axis=0)\n",
    "newdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310392, 18)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = data[ data['x/L'] == 0.3].index\n",
    "newdata = newdata.drop(indices, axis=0)\n",
    "newdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310392, 18)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = data[ data['x/L'] == 0.5].index\n",
    "newdata = newdata.drop(indices, axis=0)\n",
    "newdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310392, 18)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = data[ data['x/L'] == 0.7].index\n",
    "newdata = newdata.drop(indices, axis=0)\n",
    "newdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310392, 18)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = data[ data['X'] == 0.018].index\n",
    "newdata = newdata.drop(indices, axis=0)\n",
    "newdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['x/L'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs Location-X', fontsize=14)\n",
    "plt.xlabel('Location-X', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['y/L'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs Location-Y', fontsize=14)\n",
    "plt.xlabel('Location-Y', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['z/L'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs Location-Z', fontsize=14)\n",
    "plt.xlabel('Location-Z', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Time (s)'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs Time', fontsize=14)\n",
    "plt.xlabel('Time (s)', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEbCAYAAAABNllnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAApL0lEQVR4nO3deZgdVZ3/8fcnYTdIooRFwQFNWIMIQRZFSAsygAioiChiQJw4io6KzgDjhssM6IgO/HBYBIbFBSKOEjMoozGRQRZNI4RoRAIGh4CsSaRZZPv+/jjnQuXm3u7q7tvV3dWf1/PU01Wn6p6lqrq+t6pO1VVEYGZmVoVxw10BMzMbOxx0zMysMg46ZmZWGQcdMzOrjIOOmZlVxkHHzMwq46BjNsQkLZB0dmF6A0lXSlolKSRt1SqtA+UeK6lnsPmMJJLGSTpP0sN5Pc1ok3axpLn9yDckHTGUdbcsIjxUMADRx3DxcNdxCNp8LNAzjOXvmtftG9rMvwK4foB5X1zYdk8DDwDzgROAtZuWfQmwYWH6w3n5nYDNgPGt0jrQ/vWBTSpa1xsCXwR+BzwB3A8sAN4FjOtgOYcATwGvy+tpnTZpGwET+5HvZsC6HV4nFwNzq1j/o2lYayCBygZk88L4IcA3m9KeqLY6AydpnYh4aqSXGRE3S7oFeB/wv035vRQ4HPjgIKr1M+AYUtCYDLwR+DxwjKT9IuKxXI9Hmj43BVgSEbcV6rNG2mBFxBNUsF9JmghcB0wCPg38ihQE9gY+A9wALOtQcVOA+yLi+kL5a6Tl8kuLiD93qH7Wl+GOemNxAI5Iq361tLcA3cCTwB+BfwHWKcxfBnyW9O3pUeD/gHcCE4HLgR7gDuCAwmdmkL6JHwLckvPuBqY3lf064BfA48By4BzgxYX5C3LaV4EHgV/n9BOBRcBj+XMXkL9dFsouDqcW2vLJpjosAM5uau+pwEXASuB7ZeraYl2fkNfNhKb0jwJ/AV6Up3cC5uW0HuBWoKuXfC+mxbdYYBrpgPf5Vm3L48V1sqBVWj/W09vyNngCeCSvm03zvGNpOtMEPgAszXVcCvxd0/wAZgHfy9v1LuA9fezP/5GX3aLFvPWA9fL4JOASYEWu78+AHcvui6x+dhl5/ayR1mr7AAI+Qfof+StwD3BaU7uPKEy/nPR/tSIP/w1MLcw/FVgMHAXcSfqf/CGwcWF+8/4/Y7iPPSNhGPYKjMWBpqAD/C3pYHcc8CqgC7gd+GphmWX5oPIhYCpwBimIXA28l/QN8ELSJZrGP/mMvLP/PpcxLR9M7gM2yMvsRDrIfiLnuwfpm+mVhbIX5H+qM4DtgO1z+sdI3+63AvYlHfwuy/PWIR3YHyNdutiMfOCnfND5C/BPuW1Ty9S1xbqeSDrAHd+Uvgj4ZmH6NuBbuX1TgLcCe/WS78W0uXQCzAEWt2ob6VLbRcD1eZ28pFVamfWUl30qr4+t8vZ9P22CTm7T06RLedsAH8nTbyksE6QD8nvyejgtl/GKNm0dR9ovzy+x319F2hf3ydtyDunL0/pl9kXSJbPP589sRjq7XCOt1fbJ7VhJOuudAuwFfKip3Ufk8Q2AP+Q8Xp33iQuAu3nh/+bUXNcf5GX2yvPPy/MnkC7f/pQX9v91+lpHY2EY9gqMxYE1g861wGealjk879TK08uA7xbmT8j/KGcV0rbKabvl6Rl5+uimz60E3p+nLwUubCr7Nflzm+TpBcCiEu06kPQtclyePpYW93QoH3R+1LRMn3VtU69vUbh3A7w2f2aPQtpfgJn92IarHdSa5p0OPN5L284mn830kdbreuKFe1Z/06Yeq61/4JfARS3acV1hOlj9DGAt0llHy7MdYJP8mY/3sb6m5uX2KaRtBKzq5774SfLZTGGZVmnPbx/SPv8k8Pe91K8YdN5HOiNSYf544GHgyDx9as5zo8IynwKWltlHxvLgezojw3Rgd0knFdLGkW4Eb0Y6M4H07RyAiOiR9DjpG3rD/fnvJk3539D0uduAHQplT5H0zsLyyn9fRTpzgnRZbjWS3gicAmxPOoCMJ53hbAbc266x/bCwabpsXZtdAMyXtF1E/J50UFkcETcVlvkacIGkmaTLbN/Pyw6ESAexoXYr6RLVYkn/k8evjIgH2yy/PemMqug64NCmtOJ+9oykB1lzn2pQm/RWZT/H6vviqgHui/21A7AuabuWMR3YGnhUWq15G+R6NNwdEasK0/fSfj1Z5qAzMowjXSL4Xot5xQPI003zoimtcaDrT1f4caSD8tdbzFteGH+sOEPS35Cuc3+TdK/pYdI37++SAk9vnmPNg9XaLZZ7rGm6bF2b/YJ0/+J9kj5H6lF1anGBiDhV0reBg0iXIj8n6e8jovkgXcYOpHshg9XreoqIZyUdAOwJHAAcD5wmad+IuLUf5TQHyFb7Wbt96kHSmfP2/SivXfkD3b6dNo50D/SoFvOKnUL6s54sc9AZGW4GtouIpUOU/57kg6CkF5Gu/V9aKHvHAZS9Gym4fDwins15H9K0zFOks59mD1LouSdpPdJ189/0UeaA6hoRIeki0j2m35POIC9rsdwdpMsqZ0k6h3R/pF9BR9I00mXGL/Xnc230uZ4iXce5AbhB0heA35I6mLQKOkuA15Pu/TXsTermPCAR8Zyky4H3SvpCRNxTnJ/r3Ch7HOnex7V53otJ93H+My8z0H2xL0tIl333I23fvtxM+mLyUESsHES57fb/Mc1ReWT4AvBuSV+QNE3SdpKOkPSVDuX/aUlvkrQj6SD6FPCdPO/LpEt750raRdIUSYdIOq+PPO8g7T8fk7S1pHeROhYULQPWy2VvLGmDnP5z4Oj8EF+jTmW+AA20rpB6TW1M6oH3w4h4uDFD0vqSvpHrs5WkPSh3MF5X0maSXiZpZ0knku65dOdyBqvX9SRpT0mflvRaSa8gXSbbspd6/xupO/cJkqZK+ghwNDDY/exTwJ+AmyQdJ2nHvG2OIa2LzXJAvwo4T9IbJO1Eutf2Fwa/L/YqIh4FziSdBR4n6VWSdpfUrrv8t0mXqq+StG/ev/eRdIakqf0oehkwTdK2ef9vdTY/5jjojAARcQ3wZlKvtV/l4WTSP3InnEzqeXYz6YbuIfHCMySLSL2JtiJdhrqV1NPn/pY5vVDnRaQzhxNJB7n3k27oFpe5HjiXdMntQVJPNHL+PycdhP6HdF+hr7OcAdc1f/ZeUk+/SaRLOEXP5vSLSb0Gf0A6ezixj2z3J91v+xPpfsGhpMt2+zTW7yD1tZ5Wkc5c5pK+BJwBfDEivtUqs4j4IanH2sdJ2+yjpB5cPxpMJSM9h7Qnaf2dRAo015Mu932RF/bj40j79pz8dwPgwEjPEw1q+5ZwCimofYZ05vN9YIs27Xk81+Mu0iXv35O+tEwidZ8u65u5rIWk/f/1A6x7rTR6RlkNSZpBekp+ckQ8NLy1MTPzmY6ZmVXIQcfMzCrjy2tmZlYZn+mYmVllavWczsSJE2PKlCnDXY0h89hjj/GiF71ouKsxZOrcvjq3Ddy+0a67u/uhiJhcRVm1CjqbbropCxc2vzmlPhYsWMCMGTOGuxpDps7tq3PbwO0b7STdXVVZvrxmZmaVcdAxM7PKOOiYmVllHHTMzKwyDjpmZlaZSoOOpImSrpT0e0lLJO0l6SWSfirpjvx3Ul5Wks6StFTSIkm7Dr78NYc6qGu7xiJvS2uo675Q9ZnOmcBPImI7YGfSG1hPBuZFxFTSm3pPzsseRHoj8lRgFnDOYAput8FG+4asa7vGIm9La6jzvlBZ0JG0Eel14RcCRMRT+QeSDiO9Npz89/A8fhhwaSQ3AhMlbY6ZmY1alb17TdJrgPNJv+OxM+k3Nz4KLI+IiXkZASsiYqKkucDpEXFdnjcPOCkiFjblO4t0JsTkyZOnz549u2X53d3t6zZ9+iAaVqGenh4mTJiwWlod2tXQqn11UaZto3lb1nnbQfXtq3pf6Orq6o6I3TqfcwsRUclA+nnjZ4A98vSZpB94Wtm03Ir8dy6wdyF9HrBbb2Vss8020Q60H0aL+fPnr5FWh3Y1tGpfXZRp22jelnXedhHVt6/qfQFYGBXFgirv6dwD3BMRN+XpK4Fdgfsbl83y3wfy/OWkn95t2CKnmZnZKFVZ0ImIPwP/J2nbnLQf6VLbHGBmTptJ+mlecvp7cy+2PYFVEXHfwMvvX/poUdd2jUXeltZQ532h6hd+fgT4tqR1SL8/fhwp8M2WdDxwN3BkXvZq4GBgKfB4XnZQ6rDBWqlru8Yib0trqOu+UGnQiYhbSPd2mu3XYtkAThjqOpmZWXX8RgIzM6uMg46ZmVXGQcfMzCrjoGNmZpVx0DEzs8o46JiZWWUcdMzMrDIOOmZmVhkHHTMzq4yDjpmZVcZBx8zMKuOgY2ZmlXHQMTOzyjjomJlZZRx0zMysMg46ZmZWGQcdMzOrjIOOmZlVxkHHzMwq46BjZmaVcdAxM7PKOOiYmVllHHTMzKwyDjpmZlaZSoOOpGWSbpN0i6SFOe0lkn4q6Y78d1JOl6SzJC2VtEjSroMvf82hDurarrHI29Ia6rovDMeZTldEvCYidsvTJwPzImIqMC9PAxwETM3DLOCcwRTaboON9g1Z13aNRd6W1lDnfWEkXF47DLgkj18CHF5IvzSSG4GJkjYfhvqZmVmHKCKqK0z6I7ACCOC8iDhf0sqImJjnC1gRERMlzQVOj4jr8rx5wEkRsbApz1mkMyEmT548ffbs2S3L7u5uX6/p0wfZsIr09PQwYcKE1dLq0K6GVu2rizJtG83bss7bDqpvX9X7QldXV3fh6tPQiojKBuDl+e8mwK3APsDKpmVW5L9zgb0L6fOA3XrLf5tttol2oP0wWsyfP3+NtDq0q6FV++qiTNtG87as87aLqL59Ve8LwMKoKA5UenktIpbnvw8APwB2B+5vXDbLfx/Iiy8Htix8fIucZmZmo1RlQUfSiyRt2BgHDgAWA3OAmXmxmcBVeXwO8N7ci21PYFVE3DfQ8ttdRazw6uKQqGu7xiJvS2uo876wVoVlbQr8IN22YS3gOxHxE0m/BmZLOh64GzgyL381cDCwFHgcOG6wFajDBmulru0ai7wtraGu+0JlQSci7gJ2bpH+MLBfi/QATqigamZmVpGR0GXazMzGCAcdMzOrjIOOmZlVxkHHzMwq46BjZmaVcdAxM7PKOOiYmVllHHTMzKwyDjpmZlYZBx0zM6uMg46ZmVVmTAWd/fdf/ffG999/uGvUGXVt11g0adLq23LSpOGukQ2Xuv5fj5mgs//+MG/e6mnz5o3+DVnXdo1FkybBypWrp61c6cAzFtX5/3rMBJ3mDdhX+mhR13aNRc0Bp690q686/1+PmaBjZmbDz0HHzMwq46BjZmaV6VfQkbSxpD0krTtUFTIzs/oqFXQkbShpNvAAcD3w8px+rqRTh656ZmZWJ2XPdL5MCjS7Ak8U0ucCb+10pczMrJ7WKrncocBbI+IWSVFIXwK8svPVMjOzOip7pjMJeLhF+obAs52rjpmZ1VnZoPNr0tlOQ+Ns5wOkezxmZmZ9Knt57Z+BayTtmD9zYh7fHdhnqCpnZmb1UupMJyKuB/YC1gHuBPYD7gX2ioib+1OgpPGSfiNpbp7eWtJNkpZKukLSOjl93Ty9NM/fqj/lmJnZyNNn0JG0tqQrgCciYmZETIuIHSLiPRFx2wDK/CipA0LDl4GvR8QUYAVwfE4/HliR07+elzMzs1Gsz6ATEU8DB/DCfZwBk7QF8Gbggjwt4I3AlXmRS4DD8/hheZo8f7+8vJmZjVKK6DuWSLoQWBIRXx1UYdKVwGmkXm+fBI4FbsxnM0jaEvhxREyTtBg4MCLuyfPuBPaIiIea8pwFzAKYPHny9NmzZ7csu7u7fb2mTx9Mq6rT09PDhAkTVkurQ7saWrWvLsq0bTRvyzpvO6i+fVXvC11dXd0RsVvnc15T2Y4EfwI+LekNwELgseLMiPhaXxlIOgR4ICK6Jc3oZz3biojzgfMBtt1225gxo3XWXV295dGp2gytBQsW0Ny+OrSroVX76qJM20bztqzztoPq2zea94W+lA06x5Lut7w6D0UB9Bl0gNcDh0o6GFgPeDFwJjBR0loR8QywBbA8L78c2BK4R9JawEa0flbIzMxGiVJBJyK2HmxBEXEKcApAPtP5ZEQcLel7wBHA5cBM4Kr8kTl5+oY8/+dR5lqgmZmNWCPhpw1OIj33sxR4KXBhTr8QeGlOPxE4eZjqZ2ZmHVLqTEfSWb3Nj4h/6E+hEbEAWJDH7yI9ZNq8zJPAO/qTr5mZjWxl7+ns1DS9NrAdMB74TUdrZGZmtVX2ns4afSkkrUe6BPa/na6UmZnV04Dv6eTLX/8KfKpz1TEzszobbEeCjYH6PhFmZmYdVbYjwYnNScDmwNHA1Z2ulJmZ1VPZjgQfaZp+DngQ+E/Sa23MzMz6VNnDoWZmZqXu6Uj6rKQNWqSvL+mzna+WmZnVUdmOBJ+jdYeBDfI8MzOzPpUNOqL17+nsAjzSueqYmVmd9XpPR9KjpGATwF2SioFnPOlt0ecOXfXMzKxO+upI8GHSWc5FpIdAVxXmPQUsi4gbhqhuZmZWM70GnYi4BEDSH4Hr809Xm5mZDUjZLtO/aIxL2gxYp2n+nzpcLzMzq6GybyR4MfD/gCNpCjjZ+E5WyszM6qls77UzgJ2Bw4EngXcD/wjcA7xzSGpmZma1U/Y1OAcB74qI/5X0LNAdEVdIug/4AHDlkNXQzMxqo+yZzkTg7jy+ivSz0gA3AK/rcJ3MzKymygadO4FX5vElwFGSBLwNPxxqZmYllQ06FwOvzuOnky6pPQX8G/DlzlfLzMzqqGyX6a8Xxn8uaTtgN+COiLhtqCpnZmb10mfQkbQ2cB3w3oi4HZ5/LsfP5piZWb/0eXktv4Vga1q/8NPMzKy0svd0LgH+bigrYmZm9Vf2OZ0XAUdLehPQDTxWnBkR/9BXBpLWA64F1s3lXhkRn5O0NXA5qRt2N3BMRDwlaV3gUmA68DDwzohYVrK+ZmY2ApU909keuBlYQeo6vVNhmFYyj78Cb4yInYHXAAdK2pPU++3rETEl5398Xv54YEVO/zruJWdmNuqV7b3WNdiCIiKAnjy5dh4CeCPptTqQLuOdCpwDHJbHIb3x4GxJyvmYmdkopP4cwyVtDLwKuCUi/trvwqTxpEtoU4BvkJ7zuTGfzSBpS+DHETFN0mLgwIi4J8+7E9gjIh5qynMWMAtg8uTJ02fPnt2y7O7u9vWaPr2/LRkePT09TJiw+q+G16FdDa3aVxdl2jaat2Wdtx1U376q94Wurq7uiNit8zm3EBF9DsCGwPeA54BngVfm9HOBU8vk0ZTfRGA+sDewtJC+JbA4jy8GtijMuxPYuLd8t9lmm2gH2g+jxfz589dIq0O7Glq1ry7KtG00b8s6b7uI6ttX9b4ALIx+HscHOpS9p/Nl4GXArsAThfS5wFsHEOhW5qCzFzBRUuMy3xbA8jy+PAch8vyNSB0KzMxslCobdA4FPhYRt7D68zpLeOGdbL2SNFnSxDy+PvCm/Pn5wBF5sZnAVXl8Tp4mz/95jshmZjZKle0yPYnWZxkbki63lbE5cEm+rzMOmB0RcyX9Drhc0peA3wAX5uUvBC6TtJT0UtGjSpZjZmYjVNmg82vS2c6/5+nGGccHgOvLZBARi4BdWqTfBezeIv1J4B0l62dmZqNA2aDzz8A1knbMnzkxj+8O7DNUlTMzs3opdU8nIq4n/VjbOqReZPsB9wJ7RcTNQ1c9MzOrk7JnOkT6CYOZfS5oZmbWRumgk9+d9m5gh5z0O+C7EfFE+0+ZmZm9oNTlNUm7ki6rnUG6j7M78FXgrjzPzMysT2Wf0zkf+CXpDQH7RMQ+pAc3r83zzMzM+lT28tqOpF8Off4nDSLiMUlfABYOSc3MzKx2yp7p/J70GpxmmwN/6Fx1zMyszsqe6XwaOCuf2dyY0/bM6SdLekljwYh4pLNVNDOzuigbdH6U/36HF95GoPz3qsJ0AOM7UzUzM6ubskFn0D/iZmZmVvaXQ38x1BUxM7P668/DoesA04BNaOqAEBFXd7heZmZWQ6WCjqQ3AZeRAk4z38cxM7NSynaZ/gbpV0K3BjYA1i8MGwxN1czMrG7KXl7bHPjXiLh7KCtjZmb1VvZMZy7ppw3MzMwGrOyZzt8D35Y0HVgMPF2cGRGXdrpiZmZWP2WDzt+SfrjtYOBxXnhAlDzuoGNmZn0qe3ntq8DZwIYRMSEiNiwMLx7C+pmZWY2UDToTgXOLb5k2MzPrr7JB5/vA/kNZETMzq7+y93TuAv5F0j7AItbsSPC1TlfMzMzqp2zQeR/wKKnbdHPX6QAcdMzMrE9lX/i59WALkrQlqZfbpqRAdX5EnJl/i+cKYCtgGXBkRKyQJOBMXugxd2xE3DzYepiZ2fApe0/neZI2ldTvzwHPAJ+IiB1IPwB3gqQdgJOBeRExFZiXpwEOAqbmYRZwzgDKNDOzEaRU8JC0tqSvSHoUWE46K0HSlyV9qEweEXFf40wlIh4FlgAvBw4DLsmLXQIcnscPAy6N5EZgoqTNS7XKzMxGJEVE3wtJXwLeTjoL+Q6wU0TcJentwEkRsXu/CpW2Aq4l/VTCnyJiYk4XsCIiJkqaC5weEdflefNyWQub8ppFOhNi8uTJ02fPnt2yzO7u9vWZPr0/tR8+PT09TJgwYbW0OrSroVX76qJM20bztqzztoPq21f1vtDV1dUdEbt1PucWIqLPAbgT2DePPwq8Mo9vC6wsk0chrwlAN/C2PL2yaf6K/HcusHchfR6wW295b7PNNtEOtB9Gi/nz56+RVod2NbRqX12Uadto3pZ13nYR1bev6n0BWBj9OI4PZih7b+ZlQKs3TK9F/34Ibm3SMz/fjoj/ysn3Ny6b5b8P5PTlwJaFj2+R08zMbJQqG3R+C+zTIv1I0llLn/KlswuBJbH6cz1zgJl5fCZwVSH9vUr2BFZFxH0l62tmZiNQr2cpki4CPgp8HvhW7vY8HniHpO2AdwNvLlnW64FjgNsk3ZLT/hk4HZgt6XjS2dSRed7VpO7SS0ldpo8rWY6ZmY1QfV0amwmcHBE/knQkKUg8B3wOuBl4S0T8rExBkToEqM3s/VosH8AJZfI2M7PRoa+g83yQiIhrgGuGtjpmZlZnZe7p9N2n2szMrIQyPc/+nPoAtBcR4ztTHTMzq7MyQWcWsHKI62FmZmNAmaDzo4h4oO/FzMzMetfXPR3fzzEzs47pK+j0fjPHzMysH3q9vBYRA/kJAzMzs5YcVMzMrDIOOmZmVhkHHTMzq4yDjpmZVcZBx8zMKuOgY2ZmlXHQMTOzyjjomJlZZRx0zMysMg46ZmZWGQcdMzOrjIOOmZlVxkHHzMwq46BjZmaVcdAxM7PKOOiYmVllKgs6ki6S9ICkxYW0l0j6qaQ78t9JOV2SzpK0VNIiSbtWVU8zMxs6VZ7pXAwc2JR2MjAvIqYC8/I0wEHA1DzMAs6pqI5mZjaEKgs6EXEt8EhT8mHAJXn8EuDwQvqlkdwITJS0eSUVNTOzIaOIqK4waStgbkRMy9MrI2JiHhewIiImSpoLnB4R1+V584CTImJhizxnkc6GmDx58vTZs2e3LLu7u329pk8feJuq1NPTw4QJE1ZLq0O7Glq1ry7KtG00b8s6bzuovn1V7wtdXV3dEbFb53Ne01pVFFJGRISkfkfAiDgfOB9g2223jRkzZrRcrqurtzz6W+rwWLBgAc3tq0O7Glq1ry7KtG00b8s6bzuovn2jeV/oy3D3Xru/cdks/30gpy8Htiwst0VOMzOzUWy4g84cYGYenwlcVUh/b+7FtiewKiLuG44KmplZ51R2eU3Sd4EZwMaS7gE+B5wOzJZ0PHA3cGRe/GrgYGAp8DhwXFX1NDOzoVNZ0ImId7WZtV+LZQM4YWhrZGZmVRvuy2tmZjaGOOiYmVllHHTMzKwyDjpmZlYZBx0zM6uMg46ZmVXGQcfMzCrjoGNmZpVx0DEzs8o46JiZWWUcdMzMrDIOOmZmVhkHHTMzq4yDjpmZVcZBx8zMKuOgY2ZmlXHQMTOzyjjomJlZZRx0zMysMg46ZmZWGQcdMzOrjIOOmZlVxkHHzMwq46BjZmaVGdFBR9KBkm6XtFTSycNdHzMzG5wRG3QkjQe+ARwE7AC8S9IOw1srMzMbjBEbdIDdgaURcVdEPAVcDhw2zHUyM7NBUEQMdx1aknQEcGBEvD9PHwPsEREfblpuFjALYPLkydNnz57dMr/u7vZlTZ/emToPtZ6eHiZMmLBaWh3a1dCqfXVRpm2jeVvWedtB9e2rel/o6urqjojdOp/zmkZ90Cnadttt4/bbb2+TX/uyRugqWMOCBQuYMWPGaml1aFdDq/bVRZm2jeZtWedtB9W3r+p9QVJlQWckX15bDmxZmN4ip5mZ2Sg1koPOr4GpkraWtA5wFDBnoJm1+3Yw0r9B9qWu7RqLvC2toc77wlrDXYF2IuIZSR8GrgHGAxdFxG8Hl2dHqjbi1LVdY5G3pTXUdV8YsUEHICKuBq4e7nqYmVlnjOTLa2ZmVjMOOmZmVhkHHTMzq4yDjpmZVWbEPhw6EJIeBVo/HVoPGwMPDXclhlCd21fntoHbN9ptGxEbVlHQiO69NgC3V/VU7XCQtNDtG53q3DZw+0Y7SQurKsuX18zMrDIOOmZmVpm6BZ3zh7sCQ8ztG73q3DZw+0a7ytpXq44EZmY2stXtTMfMzEYwBx0zM6vMsAcdSQdKul3SUkknt5i/rqQr8vybJG1VmHdKTr9d0t/2lWf+mYSbcvoV+ScTei2jJu07UdLvJC2SNE/S39SpfYX5b5cUkjrWtXWktE/SkXkb/lbSd+rUPkmvkDRf0m/yPnrwKGzbh3NaSNq4kC5JZ+V5iyTt2om2jaD2HZ3bdZuk6yXt3GfFI2LYBtJPFtwJvBJYB7gV2KFpmQ8B5+bxo4Ar8vgOefl1ga1zPuN7yxOYDRyVx88FPthbGTVqXxewQR7/YN3al6c3BK4FbgR2q1P7gKnAb4BJeXqTmrXv/ML4DsCyUdi2XYCtgGXAxoUyDgZ+DAjYE7hplG67du17XWG/PKhM+wbd+EGuuL2AawrTpwCnNC1zDbBXHl+L9FSwmpdtLNcuz/yZh4C1mstuV0Zd2tdU3i7AL+u0/fL0vwNvBhbQuaAzItoHfAV4f43//84DTiqkXz+a2taU5zJWPyifB7yrMH07sHld2tc0bxKwvK+6D/fltZcD/1eYvientVwmIp4BVgEv7eWz7dJfCqzMeTSX1a6MwRop7Ss6nvTNqxNGRPvyJYstI+K/B9+k1nVvLrPVMkO4/bYBtpH0S0k3SjpwkO1ao+4tylxjmSFs36nAeyTdQ/r9rI8MplHN9W5R3hrLDLJtg63HQIyU9hWVOrbU7TU41gtJ7wF2A/Yd7rp0iqRxwNeAY4e5KkNpLdIlthnAFsC1knaKiJXDWakOehdwcUScIWkv4DJJ0yLiueGumJUjqYsUdPbua9nhPtNZDmxZmN4ip7VcRtJawEbAw718tl36w8DEnEdzWe3KGKyR0j4k7Q98Cjg0Iv46qFa1qHurMpuXGaL2bQhMAxZIWka6bj5HnelMMBLaB+kb55yIeDoi/gj8gRSEBmuktO940v0eIuIGYD3SCzYHo8q2DbYeAzFS2oekVwMXAIdFRN/HzU5cGx7Edcm1gLtIN7MaN652bFrmBFa/GTY7j+/I6jfD7iLdCGubJ/A9Vr+R+aHeyqhR+3Yh3SCcWsft11TeAjp3T2dEtA84ELgkj29MugTy0hq178fAsXl8e+BeBnlPteq2FfJcxur3dN7M6h0JfjUa981e2vcKYCnwutJ178QKGOTKO5j0ze1O4FM57Qukb+SQvvV8LzfsV8ArC5/9VP7c7cBBveWZ01+Z81ia81y3rzJq0r6fAfcDt+RhTp3a11SfBXQo6IyU9pEOWF8DfgfcRj5w16h9OwC/JB3kbgEOGIVt+wfSGekzpKB5QWHbfSMvf9so3jfbte8CYAUvHFsW9lVvvwbHzMwqM9z3dMzMbAxx0DEzs8o46JiZWWUcdMzMrDIOOmZmVhkHHRs2khZIOrswvYGkKyWtym+z3apVWgfKPVZSz2DzGUkkjZN0nqSH83qa0SbtYklz+5FvSDpiKOtuY4u7TFtHSboYmJknnyH14f8tcCVwfkQ8XVj2JcDTEfFonv4w8FlgP+DBPHywOS0inh1kHdcHNoyIBwaTT8myNgT+CXg76aG7vwBLSC+CvCI69KoXSYcA/0V6Vc5dwCPAAS3S1if9368sme9mwIro3FssGvvIxhFxSKfytNHD716zofAz4BjSU86TgTcCnweOkbRfRDwGEBGPNH1uCrAkIm5rJEhaI22wIuIJ4IlO5deOpInAdaS3736a9IDeU6T3U30GuIH0hHcnTAHui4jrC+WvkZbLLy0i/tyh+pklnXo61oOHiAC4GJjbIn0a6YD3+ULaAuDswngUhgWt0vKyy4BPNuX/fF55+m3AIlJweQT4BbBpnncs0NP0+Q+Qntx+Kv/9u6b5AcwiPeH9GOnM4T19rIv/yMtu0WLeesB6eXwScAnprPAJUtBufv3I63IbHie9D+sc4MWFdV5cT8tapbXaPqQn5j8B3AH8lfTU+WlN7T6iMP1y4PJc1xXAf1N4vRLpjdGLSa9duRN4FPgh+dUpeX40DTOGe7/1UN3gezpWiYhYDPyEdJmplbcB/0n69r95nm6V1qd8Sehy0oF8e2Af4LJeln8rcDbpN3mmAWcC/yHpLU2Lfha4CtgZuAK4SNIr2uQ5jnTg/XZE3NM8PyKejIgn8+TFwB7AYcDupMDyk3wZEEk7Af8DzMllvw14DXBR/vxHSa8/uYe0nl7bJq2VfyWddZ1GeifXO1j99fbFNm0AzAeeJL2pfC/gPuBneV7DVsA7gbeSLvHtAvxLnvdV0ss9f5brtTlQPBOzmvPlNavS74D9W82IiEckPQ48FYVLOq3SSngZsDZwZUTcndMW97L8J4HLIqLRqeEPkqYDJwE/Kix3WUR8K9frM6QD+z7At1rkuTHpDGZJbxWVNBU4FNg3Iq7NaccAfwKOJr3b6h9J93/OKHzug8BvJG0SEQ9IehR4tmndrZHWVPYE4OPAxyKiEcCWkoJ8K0eRzoyOi0inLZI+ADwAHEJ+UzTpuHJsRKzKy5wPHAcQET2SngD+2s9tajXhMx2rkkiXU4baraRv0oslfV/SByVN7mX57UkvnCy6jvQiyqJFjZFIP4r1ILBJmzxVsq7bA89RONDng/VthfKnk37krKcxFOr7qpLltLID6U3D80ouP53UGeLRQj1WkYJrsR53NwJOdi/t15ONMT7TsSrtQLoXMljPseZBfe3GSEQ8K+kA0qvkDyD9XstpkvaNiFv7UU5zgHy6xfx2X9weBFaSgspANcofRzrj+XqLZTrx2yxljSO9SfioFvOKnUL6s55sjPGOYJWQNI30uzBXdiC7B0n3Ahp5rwdsV1wgkhsi4vOk+xn3ku4ztLIEeH1T2t6ky4EDEqkr9OXA0ZK2aJ4vab1c7yWk/8O9CvNeDOxUKP9mUseCpS2GwfTCW0LqPLBfyeVvJvWSe6hFPZp7IvbmKVLPRhuDHHRsKKwraTNJL5O0s6QTSb3Lukk3kgfr56SD+QxJO5JuqD9/1i5pT0mflvTafKP/UNIvIrYLIv9G6s59gqSpkj5Cup/ylUHW81OkezM3STpO0o6SpuR7Nt3AZhFxB6lzwnmS3pA7DXyL9DzPd3I+XwZ2l3SupF1yHodIOm8wlYv0fNSZpLPA4yS9StLu+X5RK98m/S7TVZL2lbS1pH0knZHvTZW1DJgmaVtJG0tau68PWH348poNhf1JvZqeJV1iWkzqKnt+RPTrOZE2TiP1kLoK6CH1jHpZYf4q0pnLR4CJpN5YX2x0AmgWET/MgeaTpB5sd5N+1fJHrZYvK3eO2JP0cOhJuc5/IQW/L5ICEqSb7P9O6p22Hul+zYGNs5iIWCRpH+BLpG7T40mXKX8wmPplp5C6Pn+G9PPE9wOXtmnP47kep5O6jm9EOoOcn/Mo65ukB1YXAhOALtKXEhsD/EYCMzOrjC+vmZlZZRx0zMysMg46ZmZWGQcdMzOrjIOOmZlVxkHHzMwq46BjZmaVcdAxM7PK/H8VswrO9Nz61QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Alpha'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs Diffusion Coefficient', fontsize=14)\n",
    "plt.xlabel('Diffusion Coefficient', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.xlim([0, 1.2E-4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['T_left'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs T_left', fontsize=14)\n",
    "plt.xlabel('T_left', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['T_right'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs T_right', fontsize=14)\n",
    "plt.xlabel('T_right', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['T_bottom'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs T_bottom', fontsize=14)\n",
    "plt.xlabel('T_bottom', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['T_top'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs T_top', fontsize=14)\n",
    "plt.xlabel('T_top', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['T_back'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs T_back', fontsize=14)\n",
    "plt.xlabel('T_back', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['T_front'], data['Temperature'], color='blue')\n",
    "plt.title('Temperature Vs T_front', fontsize=14)\n",
    "plt.xlabel('T_front', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((310392, 11), (310392,))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seperating independent and dependent variables, Temperature is the only dependent variable in our case\n",
    "x = data[['T_left', 'T_right', 'T_bottom', 'T_top', 'T_back', 'T_front', 'Time (s)', 'Alpha', 'x/L', 'y/L', 'z/L']]\n",
    "y = data['Temperature']\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "#storing starting time\n",
    "begin = time.time()\n",
    "\n",
    "#Creating Training-Testing data from the new data frame\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_x, test_x, train_y, test_y = train_test_split(x, y , test_size = 0.25, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor(n_estimators=1000, max_depth= 12, random_state=100, oob_score = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=12, n_estimators=1000, oob_score=True,\n",
       "                      random_state=100)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.00000000e+02 5.42565117e-35 2.09949638e+02 ... 2.95893751e+02\n",
      " 4.00000000e+02 1.28591683e+02]\n"
     ]
    }
   ],
   "source": [
    "y_pred = rf.predict(test_x)\n",
    "print (y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.980172123407949"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(test_y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing end time\n",
    "time.sleep(1)\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total runtime of Random Forest program (in seconds) is 159.6921079158783\n"
     ]
    }
   ],
   "source": [
    "#Calculating runtime\n",
    "print (\"Total runtime of Random Forest program (in seconds) is\", end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 8.920822556472674\n",
      "Mean Squared Error: 424.29739208716126\n",
      "Root Mean Squared Error: 20.59848033441208\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(test_y, y_pred)) \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(test_y, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(test_y, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = pd.DataFrame({'feature':train_x.columns,'importance':np.round(rf.feature_importances_,3)})\n",
    "importances = importances.sort_values('importance',ascending=False).set_index('feature')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>z/L</th>\n",
       "      <td>0.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y/L</th>\n",
       "      <td>0.249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x/L</th>\n",
       "      <td>0.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time (s)</th>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_left</th>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          importance\n",
       "feature             \n",
       "z/L            0.323\n",
       "y/L            0.249\n",
       "x/L            0.224\n",
       "Time (s)       0.047\n",
       "T_left         0.033"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='feature'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9804338846100279"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18132</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235249</th>\n",
       "      <td>7.766960e-35</td>\n",
       "      <td>5.425651e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275916</th>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>2.099496e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509164</th>\n",
       "      <td>3.091750e+02</td>\n",
       "      <td>2.181491e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446466</th>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>3.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79449</th>\n",
       "      <td>4.000000e+02</td>\n",
       "      <td>4.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409949</th>\n",
       "      <td>5.000000e+02</td>\n",
       "      <td>5.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199684</th>\n",
       "      <td>2.969400e+02</td>\n",
       "      <td>2.958938e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85119</th>\n",
       "      <td>4.000000e+02</td>\n",
       "      <td>4.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96315</th>\n",
       "      <td>1.411250e+02</td>\n",
       "      <td>1.285917e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77598 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Actual     Predicted\n",
       "18132   1.000000e+02  1.000000e+02\n",
       "235249  7.766960e-35  5.425651e-35\n",
       "275916  2.000000e+02  2.099496e+02\n",
       "509164  3.091750e+02  2.181491e+02\n",
       "446466  3.000000e+02  3.000000e+02\n",
       "...              ...           ...\n",
       "79449   4.000000e+02  4.000000e+02\n",
       "409949  5.000000e+02  5.000000e+02\n",
       "199684  2.969400e+02  2.958938e+02\n",
       "85119   4.000000e+02  4.000000e+02\n",
       "96315   1.411250e+02  1.285917e+02\n",
       "\n",
       "[77598 rows x 2 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':test_y, 'Predicted':y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(5, 7))\n",
    "\n",
    "\n",
    "ax = sns.distplot(y, hist=False, color=\"r\", label=\"Actual Value\")\n",
    "sns.distplot(y_pred, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax)\n",
    "\n",
    "\n",
    "plt.title('Actual vs Fitted Values for Temperature')\n",
    "\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_test = np.array([0.8,0.33,8e-6,400,150])\n",
    "#x_test = x_test.reshape((1,-1))\n",
    "#y_pred = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted temperature for the given parameters is 100.0\n"
     ]
    }
   ],
   "source": [
    "print (\"The predicted temperature for the given parameters is\", y_pred[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from yellowbrick.datasets import load_energy\n",
    "# from yellowbrick.model_selection import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# x = data[['T_left', 'T_right', 'T_bottom', 'T_top', 'T_back', 'T_front', 'Time (s)', 'Alpha', 'x/L', 'y/L', 'z/L']]\n",
    "# y = data['Temperature']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameter_range = np.arange(1, 15, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# begin = time.time()\n",
    "# viz = validation_curve(RandomForestRegressor(), x, y, param_name = \"max_depth\", param_range = parameter_range, cv = 10, scoring = \"r2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#viz.fit(x,y)\n",
    "# viz.show()\n",
    "# print(\"Validation curve time taken: \", time.time() - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Time: \", time.time() - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameter_range = np.arange(1, 8000, 1000)\n",
    "# begin = time.time()\n",
    "# viz = validation_curve(RandomForestRegressor(), x, y, param_name = \"n_estimators\", param_range = parameter_range, cv = 5, scoring = \"r2\")\n",
    "# print(\"N Estimators time taken: \", time.time() - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from yellowbrick.model_selection import learning_curve\n",
    "\n",
    "# x = data[['T_left', 'T_right', 'T_bottom', 'T_top', 'Time (s)', 'Alpha', 'x/L', 'y/L']]\n",
    "# y = data['Temperature']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(learning_curve(RandomForestRegressor(), x, y, cv=10, scoring='r2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import Lasso\n",
    "# from sklearn.model_selection import train_test_split\n",
    "\n",
    "# from yellowbrick.regressor import PredictionError\n",
    "\n",
    "# # Load a regression dataset\n",
    "# x = data[['T_left', 'T_right', 'T_bottom', 'T_top', 'Time (s)', 'Alpha', 'x/L', 'y/L']]\n",
    "# y = data['Temperature']\n",
    "\n",
    "# # Instantiate the linear model and visualizer\n",
    "# model = Lasso()\n",
    "# visualizer = PredictionError(model)\n",
    "\n",
    "# visualizer.fit(train_x, train_y)  # Fit the training data to the visualizer\n",
    "# visualizer.score(test_x, test_y)  # Evaluate the model on the test data\n",
    "# visualizer.show()                 # Finalize and render the figure"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}