performance_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data formatting for plotting graphs in overleaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "performance = pd.read_csv('performance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>real</th>\n",
       "      <th>predicted</th>\n",
       "      <th>regressor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>91.300000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>75.990000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>58.550000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>63.300000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>82.010000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>81</td>\n",
       "      <td>85.600000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>92</td>\n",
       "      <td>79.470000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>67</td>\n",
       "      <td>72.890000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>85.120000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>60</td>\n",
       "      <td>66.680000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>62</td>\n",
       "      <td>47.130000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>75.150000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>61</td>\n",
       "      <td>49.730000</td>\n",
       "      <td>random_forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>93</td>\n",
       "      <td>82.796122</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>82</td>\n",
       "      <td>96.703325</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>42</td>\n",
       "      <td>62.171404</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>71.096424</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>71</td>\n",
       "      <td>101.954039</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>81</td>\n",
       "      <td>76.201293</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>92</td>\n",
       "      <td>82.941613</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>67</td>\n",
       "      <td>90.171106</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>92.943902</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>60</td>\n",
       "      <td>44.905004</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>62</td>\n",
       "      <td>37.364323</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>43.767632</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>61</td>\n",
       "      <td>67.961082</td>\n",
       "      <td>linear_regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>93</td>\n",
       "      <td>50.484355</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>82</td>\n",
       "      <td>90.796580</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>42</td>\n",
       "      <td>49.648117</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>44</td>\n",
       "      <td>66.433630</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>71</td>\n",
       "      <td>143.758012</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>81</td>\n",
       "      <td>80.953394</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>92</td>\n",
       "      <td>76.644353</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>67</td>\n",
       "      <td>123.363098</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "      <td>-21.674469</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>68.702275</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>62</td>\n",
       "      <td>53.135668</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>32</td>\n",
       "      <td>84.506061</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>61</td>\n",
       "      <td>-97.856780</td>\n",
       "      <td>gaussian_process</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    real   predicted          regressor\n",
       "0     93   91.300000      random_forest\n",
       "1     82   75.990000      random_forest\n",
       "2     42   58.550000      random_forest\n",
       "3     44   63.300000      random_forest\n",
       "4     71   82.010000      random_forest\n",
       "5     81   85.600000      random_forest\n",
       "6     92   79.470000      random_forest\n",
       "7     67   72.890000      random_forest\n",
       "8     10   85.120000      random_forest\n",
       "9     60   66.680000      random_forest\n",
       "10    62   47.130000      random_forest\n",
       "11    32   75.150000      random_forest\n",
       "12    61   49.730000      random_forest\n",
       "13    93   82.796122  linear_regression\n",
       "14    82   96.703325  linear_regression\n",
       "15    42   62.171404  linear_regression\n",
       "16    44   71.096424  linear_regression\n",
       "17    71  101.954039  linear_regression\n",
       "18    81   76.201293  linear_regression\n",
       "19    92   82.941613  linear_regression\n",
       "20    67   90.171106  linear_regression\n",
       "21    10   92.943902  linear_regression\n",
       "22    60   44.905004  linear_regression\n",
       "23    62   37.364323  linear_regression\n",
       "24    32   43.767632  linear_regression\n",
       "25    61   67.961082  linear_regression\n",
       "26    93   50.484355   gaussian_process\n",
       "27    82   90.796580   gaussian_process\n",
       "28    42   49.648117   gaussian_process\n",
       "29    44   66.433630   gaussian_process\n",
       "30    71  143.758012   gaussian_process\n",
       "31    81   80.953394   gaussian_process\n",
       "32    92   76.644353   gaussian_process\n",
       "33    67  123.363098   gaussian_process\n",
       "34    10  -21.674469   gaussian_process\n",
       "35    60   68.702275   gaussian_process\n",
       "36    62   53.135668   gaussian_process\n",
       "37    32   84.506061   gaussian_process\n",
       "38    61  -97.856780   gaussian_process"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "( 93 , 91.3 )\n",
      "( 82 , 75.99 )\n",
      "( 42 , 58.55 )\n",
      "( 44 , 63.3 )\n",
      "( 71 , 82.01 )\n",
      "( 81 , 85.6 )\n",
      "( 92 , 79.47 )\n",
      "( 67 , 72.89 )\n",
      "( 10 , 85.12 )\n",
      "( 60 , 66.68 )\n",
      "( 62 , 47.13 )\n",
      "( 32 , 75.15 )\n",
      "( 61 , 49.73 )\n"
     ]
    }
   ],
   "source": [
    "#random_forest\n",
    "for i in range(len(performance)):\n",
    "    if performance['regressor'][i]=='random_forest':\n",
    "        print('(', performance['real'][i], ',', performance['predicted'][i], ')')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "( 93 , 82.79612172 )\n",
      "( 82 , 96.70332481 )\n",
      "( 42 , 62.17140413 )\n",
      "( 44 , 71.09642427 )\n",
      "( 71 , 101.95403890000001 )\n",
      "( 81 , 76.20129278 )\n",
      "( 92 , 82.94161290000001 )\n",
      "( 67 , 90.17110562 )\n",
      "( 10 , 92.94390216 )\n",
      "( 60 , 44.90500383 )\n",
      "( 62 , 37.36432265 )\n",
      "( 32 , 43.76763158 )\n",
      "( 61 , 67.96108175 )\n"
     ]
    }
   ],
   "source": [
    "#linear regression\n",
    "for i in range(len(performance)):\n",
    "    if performance['regressor'][i]=='linear_regression':\n",
    "        print('(',performance['real'][i],',',performance['predicted'][i],')')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "( 93 , 50.48435525 )\n",
      "( 82 , 90.79657994 )\n",
      "( 42 , 49.64811706 )\n",
      "( 44 , 66.43362998 )\n",
      "( 71 , 143.758012 )\n",
      "( 81 , 80.95339391 )\n",
      "( 92 , 76.64435304 )\n",
      "( 67 , 123.36309809999999 )\n",
      "( 10 , -21.67446936 )\n",
      "( 60 , 68.70227505 )\n",
      "( 62 , 53.13566806 )\n",
      "( 32 , 84.50606091 )\n",
      "( 61 , -97.85677951 )\n"
     ]
    }
   ],
   "source": [
    "#gaussian process regressor\n",
    "for i in range(len(performance)):\n",
    "    if performance['regressor'][i]=='gaussian_process':\n",
    "        print('(', performance['real'][i], ',', performance['predicted'][i], ')')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#45 degree line\n"
   ]
  }
 ],
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