part1_Dubai_CW_UG_9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to load the dataset and learn basic information about it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# loads X train dataset (features of each instance)\n",
    "x_train = pd.read_csv('Dataset/x_train_all.csv')\n",
    "# load Y train dataset (classes of each instance)\n",
    "y_train=pd.read_csv('Dataset/y_train_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>2294</th>\n",
       "      <th>2295</th>\n",
       "      <th>2296</th>\n",
       "      <th>2297</th>\n",
       "      <th>2298</th>\n",
       "      <th>2299</th>\n",
       "      <th>2300</th>\n",
       "      <th>2301</th>\n",
       "      <th>2302</th>\n",
       "      <th>2303</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.00000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>89.658514</td>\n",
       "      <td>89.545201</td>\n",
       "      <td>89.343756</td>\n",
       "      <td>89.566770</td>\n",
       "      <td>89.812590</td>\n",
       "      <td>89.853251</td>\n",
       "      <td>89.883282</td>\n",
       "      <td>89.978431</td>\n",
       "      <td>90.170175</td>\n",
       "      <td>90.519092</td>\n",
       "      <td>...</td>\n",
       "      <td>72.234881</td>\n",
       "      <td>71.833437</td>\n",
       "      <td>71.394118</td>\n",
       "      <td>70.572136</td>\n",
       "      <td>69.660165</td>\n",
       "      <td>68.878638</td>\n",
       "      <td>68.29453</td>\n",
       "      <td>67.831889</td>\n",
       "      <td>67.548297</td>\n",
       "      <td>67.393189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>77.521341</td>\n",
       "      <td>77.430539</td>\n",
       "      <td>77.350452</td>\n",
       "      <td>77.498754</td>\n",
       "      <td>77.437292</td>\n",
       "      <td>77.279125</td>\n",
       "      <td>77.085714</td>\n",
       "      <td>77.081732</td>\n",
       "      <td>77.062371</td>\n",
       "      <td>77.155644</td>\n",
       "      <td>...</td>\n",
       "      <td>65.369022</td>\n",
       "      <td>65.439225</td>\n",
       "      <td>65.402298</td>\n",
       "      <td>65.144238</td>\n",
       "      <td>64.885963</td>\n",
       "      <td>64.530366</td>\n",
       "      <td>64.27455</td>\n",
       "      <td>63.799350</td>\n",
       "      <td>63.437102</td>\n",
       "      <td>63.506304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.00000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>122.750000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>123.750000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>82.00000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 2304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3            4  \\\n",
       "count  9690.000000  9690.000000  9690.000000  9690.000000  9690.000000   \n",
       "mean     89.658514    89.545201    89.343756    89.566770    89.812590   \n",
       "std      77.521341    77.430539    77.350452    77.498754    77.437292   \n",
       "min       5.000000     5.000000     4.000000     5.000000     5.000000   \n",
       "25%      32.000000    32.000000    32.000000    31.000000    32.000000   \n",
       "50%      57.000000    58.000000    57.000000    58.000000    58.000000   \n",
       "75%     126.000000   124.000000   122.750000   123.000000   125.000000   \n",
       "max     255.000000   255.000000   255.000000   255.000000   255.000000   \n",
       "\n",
       "                 5            6            7            8            9  ...  \\\n",
       "count  9690.000000  9690.000000  9690.000000  9690.000000  9690.000000  ...   \n",
       "mean     89.853251    89.883282    89.978431    90.170175    90.519092  ...   \n",
       "std      77.279125    77.085714    77.081732    77.062371    77.155644  ...   \n",
       "min       5.000000     5.000000     4.000000     4.000000     4.000000  ...   \n",
       "25%      32.000000    32.000000    33.000000    33.000000    33.000000  ...   \n",
       "50%      58.000000    58.000000    58.000000    59.000000    59.000000  ...   \n",
       "75%     123.750000   124.000000   124.000000   124.000000   126.000000  ...   \n",
       "max     255.000000   255.000000   255.000000   255.000000   255.000000  ...   \n",
       "\n",
       "              2294         2295         2296         2297         2298  \\\n",
       "count  9690.000000  9690.000000  9690.000000  9690.000000  9690.000000   \n",
       "mean     72.234881    71.833437    71.394118    70.572136    69.660165   \n",
       "std      65.369022    65.439225    65.402298    65.144238    64.885963   \n",
       "min       4.000000     4.000000     4.000000     5.000000     4.000000   \n",
       "25%      26.000000    26.000000    26.000000    26.000000    26.000000   \n",
       "50%      46.000000    46.000000    46.000000    45.000000    45.000000   \n",
       "75%      95.000000    93.000000    91.000000    88.000000    86.000000   \n",
       "max     255.000000   255.000000   255.000000   255.000000   255.000000   \n",
       "\n",
       "              2299        2300         2301         2302         2303  \n",
       "count  9690.000000  9690.00000  9690.000000  9690.000000  9690.000000  \n",
       "mean     68.878638    68.29453    67.831889    67.548297    67.393189  \n",
       "std      64.530366    64.27455    63.799350    63.437102    63.506304  \n",
       "min       4.000000     4.00000     5.000000     4.000000     4.000000  \n",
       "25%      25.000000    25.00000    25.000000    25.000000    25.000000  \n",
       "50%      44.000000    44.00000    43.000000    43.000000    43.000000  \n",
       "75%      84.000000    82.00000    82.000000    81.000000    81.000000  \n",
       "max     255.000000   255.00000   255.000000   255.000000   255.000000  \n",
       "\n",
       "[8 rows x 2304 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9690, 2304)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gets (number of instances, no. of features)\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks for null values\n",
    "x_train.isnull().sum().sum() > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>2294</th>\n",
       "      <th>2295</th>\n",
       "      <th>2296</th>\n",
       "      <th>2297</th>\n",
       "      <th>2298</th>\n",
       "      <th>2299</th>\n",
       "      <th>2300</th>\n",
       "      <th>2301</th>\n",
       "      <th>2302</th>\n",
       "      <th>2303</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>...</td>\n",
       "      <td>87.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 2304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1     2     3     4     5      6      7      8      9  ...  2294  \\\n",
       "0  78.0  77.0  76.0  82.0  87.0  92.0  104.0  119.0  117.0  120.0  ...  87.0   \n",
       "\n",
       "   2295  2296  2297  2298  2299  2300  2301  2302  2303  \n",
       "0  79.0  72.0  76.0  83.0  95.0  99.0  98.0  95.0  94.0  \n",
       "\n",
       "[1 rows x 2304 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gets an example of an instance\n",
    "x_train.iloc[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9690, 1)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above we confirm that:\n",
    "1. The dataset is made up of 9690 instances of 2304 features (or 9690 images with a 48x48 resolution)\n",
    "2. The dataset has no null values, so theres is no need to filter them out\n",
    "3. Each instance belongs to one class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we look at the class distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# renames the column containing labels (classes) to 'label'\n",
    "y_train.rename(columns={'0': 'label'},inplace=True)\n",
    "\n",
    "# makes a bar graph counting the number of instances for each label\n",
    "sns.countplot(data=y_train, x='label')\n",
    "plt.title('Class Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, the class distribution is very uneven, we will have to deal with this when training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we look for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates a box plot to identify potential outliers\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(data=x_train)\n",
    "plt.title('Box Plot with Outliers (Non-normalized)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use an isolation forest model to detect outliers within our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uses isolation forest algorithm to find outliers\n",
    "from sklearn.ensemble import IsolationForest\n",
    "\n",
    "# TODO\n",
    "feature_names = [f'feature_{str(col)}' for col in x_train.columns]\n",
    "x_train_copy = x_train.copy()\n",
    "x_train_copy.columns = feature_names\n",
    "\n",
    "contamination = 0.01\n",
    "iso_forest = IsolationForest(contamination=contamination, random_state=42)\n",
    "\n",
    "outliers = iso_forest.fit_predict(x_train_copy)\n",
    "\n",
    "outlier_indices = x_train_copy.index[outliers == -1]\n",
    "\n",
    "x_train_no_outliers = x_train_copy.drop(outlier_indices)\n",
    "x_train_no_outliers.to_csv('x_train_no_outliers.csv',index=False)\n",
    "\n",
    "outlier_indices_df = pd.DataFrame({'outlier_index': outlier_indices})\n",
    "\n",
    "outlier_indices_df.to_csv('outlier_indices.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creates a box plot without the outliers\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(data=x_train_no_outliers)\n",
    "plt.title('Box Plot without Outliers (Non-normalized)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of images: 9690\n",
      "Number of images excluding outliers: 9593\n",
      "Number of outliers removed: 97\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of images:\", x_train.shape[0])\n",
    "print(\"Number of images excluding outliers:\", x_train_no_outliers.shape[0])\n",
    "print(\"Number of outliers removed:\", x_train.shape[0] - x_train_no_outliers.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above, outliers are hardly noticeable in the graph, and not many were found (only 97). We can fix this by normalizing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>2294</th>\n",
       "      <th>2295</th>\n",
       "      <th>2296</th>\n",
       "      <th>2297</th>\n",
       "      <th>2298</th>\n",
       "      <th>2299</th>\n",
       "      <th>2300</th>\n",
       "      <th>2301</th>\n",
       "      <th>2302</th>\n",
       "      <th>2303</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.150391</td>\n",
       "      <td>-0.162019</td>\n",
       "      <td>-0.172510</td>\n",
       "      <td>-0.097637</td>\n",
       "      <td>-0.036321</td>\n",
       "      <td>0.027779</td>\n",
       "      <td>0.183130</td>\n",
       "      <td>0.376504</td>\n",
       "      <td>0.348157</td>\n",
       "      <td>0.382097</td>\n",
       "      <td>...</td>\n",
       "      <td>0.225873</td>\n",
       "      <td>0.109515</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>0.083321</td>\n",
       "      <td>0.205589</td>\n",
       "      <td>0.404792</td>\n",
       "      <td>0.477724</td>\n",
       "      <td>0.472859</td>\n",
       "      <td>0.432739</td>\n",
       "      <td>0.418963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.214889</td>\n",
       "      <td>-0.187848</td>\n",
       "      <td>-0.133726</td>\n",
       "      <td>-0.149251</td>\n",
       "      <td>-0.178371</td>\n",
       "      <td>-0.192203</td>\n",
       "      <td>-0.011458</td>\n",
       "      <td>0.220825</td>\n",
       "      <td>0.555781</td>\n",
       "      <td>0.446901</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363553</td>\n",
       "      <td>0.323454</td>\n",
       "      <td>0.208034</td>\n",
       "      <td>0.098671</td>\n",
       "      <td>-0.010174</td>\n",
       "      <td>0.063867</td>\n",
       "      <td>0.228791</td>\n",
       "      <td>0.504208</td>\n",
       "      <td>0.527321</td>\n",
       "      <td>0.529189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.227789</td>\n",
       "      <td>-0.187848</td>\n",
       "      <td>-0.133726</td>\n",
       "      <td>-0.162154</td>\n",
       "      <td>-0.113803</td>\n",
       "      <td>-0.011041</td>\n",
       "      <td>0.196103</td>\n",
       "      <td>0.246771</td>\n",
       "      <td>-0.054114</td>\n",
       "      <td>-0.006728</td>\n",
       "      <td>...</td>\n",
       "      <td>0.394149</td>\n",
       "      <td>0.354016</td>\n",
       "      <td>0.253904</td>\n",
       "      <td>0.144723</td>\n",
       "      <td>0.051472</td>\n",
       "      <td>0.032874</td>\n",
       "      <td>0.088767</td>\n",
       "      <td>0.190725</td>\n",
       "      <td>0.338157</td>\n",
       "      <td>0.434710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.292287</td>\n",
       "      <td>-0.252422</td>\n",
       "      <td>-0.198367</td>\n",
       "      <td>-0.123444</td>\n",
       "      <td>0.041161</td>\n",
       "      <td>0.221881</td>\n",
       "      <td>0.260966</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>-0.274715</td>\n",
       "      <td>0.122880</td>\n",
       "      <td>...</td>\n",
       "      <td>0.608317</td>\n",
       "      <td>0.308172</td>\n",
       "      <td>0.238614</td>\n",
       "      <td>0.175424</td>\n",
       "      <td>0.113119</td>\n",
       "      <td>0.048370</td>\n",
       "      <td>0.026534</td>\n",
       "      <td>0.065332</td>\n",
       "      <td>0.212048</td>\n",
       "      <td>0.324484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.201990</td>\n",
       "      <td>-0.200763</td>\n",
       "      <td>-0.211295</td>\n",
       "      <td>-0.226672</td>\n",
       "      <td>-0.165458</td>\n",
       "      <td>-0.036921</td>\n",
       "      <td>0.183130</td>\n",
       "      <td>0.246771</td>\n",
       "      <td>-0.080067</td>\n",
       "      <td>-0.097454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.424744</td>\n",
       "      <td>0.399861</td>\n",
       "      <td>0.422093</td>\n",
       "      <td>0.451734</td>\n",
       "      <td>0.452175</td>\n",
       "      <td>0.311812</td>\n",
       "      <td>0.151000</td>\n",
       "      <td>-0.028713</td>\n",
       "      <td>0.007120</td>\n",
       "      <td>0.072541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9685</th>\n",
       "      <td>2.029654</td>\n",
       "      <td>1.684798</td>\n",
       "      <td>2.128704</td>\n",
       "      <td>1.592712</td>\n",
       "      <td>0.506053</td>\n",
       "      <td>1.528831</td>\n",
       "      <td>2.129016</td>\n",
       "      <td>2.140865</td>\n",
       "      <td>2.138915</td>\n",
       "      <td>2.131807</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.569611</td>\n",
       "      <td>-0.654553</td>\n",
       "      <td>-0.678785</td>\n",
       "      <td>-0.684207</td>\n",
       "      <td>-0.688287</td>\n",
       "      <td>-0.710962</td>\n",
       "      <td>-0.720262</td>\n",
       "      <td>-0.655679</td>\n",
       "      <td>-0.654953</td>\n",
       "      <td>-0.636050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9686</th>\n",
       "      <td>0.791285</td>\n",
       "      <td>0.367488</td>\n",
       "      <td>2.128704</td>\n",
       "      <td>2.134657</td>\n",
       "      <td>2.133176</td>\n",
       "      <td>2.137016</td>\n",
       "      <td>2.129016</td>\n",
       "      <td>2.127892</td>\n",
       "      <td>2.125938</td>\n",
       "      <td>2.092924</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.539015</td>\n",
       "      <td>-0.623990</td>\n",
       "      <td>-0.632915</td>\n",
       "      <td>-0.622805</td>\n",
       "      <td>-0.611229</td>\n",
       "      <td>-0.602486</td>\n",
       "      <td>-0.611354</td>\n",
       "      <td>-0.655679</td>\n",
       "      <td>-0.623425</td>\n",
       "      <td>-0.636050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9687</th>\n",
       "      <td>2.132851</td>\n",
       "      <td>2.136816</td>\n",
       "      <td>2.141633</td>\n",
       "      <td>2.134657</td>\n",
       "      <td>2.133176</td>\n",
       "      <td>2.137016</td>\n",
       "      <td>2.141989</td>\n",
       "      <td>2.140865</td>\n",
       "      <td>2.138915</td>\n",
       "      <td>2.131807</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.477824</td>\n",
       "      <td>-0.348926</td>\n",
       "      <td>-0.449436</td>\n",
       "      <td>-0.530701</td>\n",
       "      <td>-0.564994</td>\n",
       "      <td>-0.509506</td>\n",
       "      <td>-0.455772</td>\n",
       "      <td>-0.577308</td>\n",
       "      <td>-0.450025</td>\n",
       "      <td>-0.384107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9688</th>\n",
       "      <td>2.132851</td>\n",
       "      <td>2.110986</td>\n",
       "      <td>2.141633</td>\n",
       "      <td>2.134657</td>\n",
       "      <td>2.133176</td>\n",
       "      <td>2.137016</td>\n",
       "      <td>2.141989</td>\n",
       "      <td>2.140865</td>\n",
       "      <td>2.138915</td>\n",
       "      <td>2.131807</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.523717</td>\n",
       "      <td>-0.685116</td>\n",
       "      <td>-0.694075</td>\n",
       "      <td>-0.668856</td>\n",
       "      <td>-0.534170</td>\n",
       "      <td>-0.633479</td>\n",
       "      <td>-0.642471</td>\n",
       "      <td>-0.655679</td>\n",
       "      <td>-0.654953</td>\n",
       "      <td>-0.683289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9689</th>\n",
       "      <td>2.094152</td>\n",
       "      <td>1.284439</td>\n",
       "      <td>1.921854</td>\n",
       "      <td>2.134657</td>\n",
       "      <td>2.133176</td>\n",
       "      <td>2.007615</td>\n",
       "      <td>1.674976</td>\n",
       "      <td>1.583015</td>\n",
       "      <td>0.646617</td>\n",
       "      <td>-0.654769</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.584908</td>\n",
       "      <td>-0.746241</td>\n",
       "      <td>-0.739945</td>\n",
       "      <td>-0.622805</td>\n",
       "      <td>-0.580405</td>\n",
       "      <td>-0.710962</td>\n",
       "      <td>-0.704704</td>\n",
       "      <td>-0.655679</td>\n",
       "      <td>-0.749535</td>\n",
       "      <td>-0.793515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9690 rows × 2304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5         6  \\\n",
       "0    -0.150391 -0.162019 -0.172510 -0.097637 -0.036321  0.027779  0.183130   \n",
       "1    -0.214889 -0.187848 -0.133726 -0.149251 -0.178371 -0.192203 -0.011458   \n",
       "2    -0.227789 -0.187848 -0.133726 -0.162154 -0.113803 -0.011041  0.196103   \n",
       "3    -0.292287 -0.252422 -0.198367 -0.123444  0.041161  0.221881  0.260966   \n",
       "4    -0.201990 -0.200763 -0.211295 -0.226672 -0.165458 -0.036921  0.183130   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "9685  2.029654  1.684798  2.128704  1.592712  0.506053  1.528831  2.129016   \n",
       "9686  0.791285  0.367488  2.128704  2.134657  2.133176  2.137016  2.129016   \n",
       "9687  2.132851  2.136816  2.141633  2.134657  2.133176  2.137016  2.141989   \n",
       "9688  2.132851  2.110986  2.141633  2.134657  2.133176  2.137016  2.141989   \n",
       "9689  2.094152  1.284439  1.921854  2.134657  2.133176  2.007615  1.674976   \n",
       "\n",
       "             7         8         9  ...      2294      2295      2296  \\\n",
       "0     0.376504  0.348157  0.382097  ...  0.225873  0.109515  0.009264   \n",
       "1     0.220825  0.555781  0.446901  ...  0.363553  0.323454  0.208034   \n",
       "2     0.246771 -0.054114 -0.006728  ...  0.394149  0.354016  0.253904   \n",
       "3     0.078119 -0.274715  0.122880  ...  0.608317  0.308172  0.238614   \n",
       "4     0.246771 -0.080067 -0.097454  ...  0.424744  0.399861  0.422093   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "9685  2.140865  2.138915  2.131807  ... -0.569611 -0.654553 -0.678785   \n",
       "9686  2.127892  2.125938  2.092924  ... -0.539015 -0.623990 -0.632915   \n",
       "9687  2.140865  2.138915  2.131807  ... -0.477824 -0.348926 -0.449436   \n",
       "9688  2.140865  2.138915  2.131807  ... -0.523717 -0.685116 -0.694075   \n",
       "9689  1.583015  0.646617 -0.654769  ... -0.584908 -0.746241 -0.739945   \n",
       "\n",
       "          2297      2298      2299      2300      2301      2302      2303  \n",
       "0     0.083321  0.205589  0.404792  0.477724  0.472859  0.432739  0.418963  \n",
       "1     0.098671 -0.010174  0.063867  0.228791  0.504208  0.527321  0.529189  \n",
       "2     0.144723  0.051472  0.032874  0.088767  0.190725  0.338157  0.434710  \n",
       "3     0.175424  0.113119  0.048370  0.026534  0.065332  0.212048  0.324484  \n",
       "4     0.451734  0.452175  0.311812  0.151000 -0.028713  0.007120  0.072541  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "9685 -0.684207 -0.688287 -0.710962 -0.720262 -0.655679 -0.654953 -0.636050  \n",
       "9686 -0.622805 -0.611229 -0.602486 -0.611354 -0.655679 -0.623425 -0.636050  \n",
       "9687 -0.530701 -0.564994 -0.509506 -0.455772 -0.577308 -0.450025 -0.384107  \n",
       "9688 -0.668856 -0.534170 -0.633479 -0.642471 -0.655679 -0.654953 -0.683289  \n",
       "9689 -0.622805 -0.580405 -0.710962 -0.704704 -0.655679 -0.749535 -0.793515  \n",
       "\n",
       "[9690 rows x 2304 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We perform z-normalization on our dataset\n",
    "\n",
    "mean=x_train.mean()\n",
    "std=x_train.std()\n",
    "x_train_norm=(x_train-mean)/std\n",
    "x_train_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import StandardScaler\n",
    "# scaler = StandardScaler()\n",
    "# pd.DataFrame(scaler.fit_transform(x_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalizing may have other benefits, we will see this when we get to data modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# makes a box plot of the normalized data\n",
    "sns.boxplot(data=x_train_norm)\n",
    "plt.title('Box Plot with Outliers (Normalized)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# On the normalized dataset, we run another isolation forest model to detect outliers from it.\n",
    "\n",
    "x_train_norm.columns = feature_names\n",
    "\n",
    "x_train_norm_copy = x_train_norm.copy()\n",
    "contamination = 0.41\n",
    "iso_forest = IsolationForest(contamination=contamination, random_state=42)\n",
    "\n",
    "outliers = iso_forest.fit_predict(x_train_norm_copy)\n",
    "\n",
    "outlier_indices = x_train_norm_copy.index[outliers == -1]\n",
    "\n",
    "x_train_no_outliers_norm = x_train_norm_copy.drop(outlier_indices)\n",
    "x_train_no_outliers_norm.to_csv('x_train_no_outliers_norm.csv')\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "sns.boxplot(data=x_train_no_outliers_norm)\n",
    "plt.title('Box Plot without Outliers (Normalized)')\n",
    "plt.show()\n",
    "\n",
    "outlier_indices_df = pd.DataFrame({'outlier_index_norm': outlier_indices})\n",
    "outlier_indices_df.to_csv('outlier_indices_norm.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of images: 9690\n",
      "Number of images excluding outliers: 5717\n",
      "Number of outliers removed: 3973\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of images:\", x_train_norm.shape[0])\n",
    "print(\"Number of images excluding outliers:\", x_train_no_outliers_norm.shape[0])\n",
    "print(\"Number of outliers removed:\", x_train_norm.shape[0] - x_train_no_outliers_norm.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above, normalizing had a noticeable impact in the number of outliers found, its effect on model accuracy will be tested when we get to modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we also remove the Y train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loads the previously saved pre-processed datasets\n",
    "# avoids having to run the pre-processing code again\n",
    "df_x_train_no_outliers=pd.read_csv('x_train_no_outliers.csv')\n",
    "df_x_train_no_outliers_norm=pd.read_csv('x_train_no_outliers_norm.csv')\n",
    "\n",
    "# removes previous indices, since many were skipped due to outlier mining\n",
    "# df_x_train_no_outliers.drop('Unnamed: 0',axis=1, inplace=True)\n",
    "# df_x_train_no_outliers_norm.drop('Unnamed: 0',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applies outlier mining to y train\n",
    "\n",
    "# loads the indices to be removed from each dataset\n",
    "indices_to_remove = pd.read_csv('outlier_indices.csv')\n",
    "indices_to_remove_norm = pd.read_csv('outlier_indices_norm.csv')\n",
    "\n",
    "# removes outliers from y train\n",
    "y_train_filtered_outlier = y_train[~y_train.index.isin(indices_to_remove['outlier_index'])]\n",
    "\n",
    "# removes outliers found after normalizing from y train\n",
    "y_train_filtered_outlier_norm = y_train[~y_train.index.isin(indices_to_remove_norm['outlier_index_norm'])]\n",
    "\n",
    "# saves to csv for future use\n",
    "y_train_filtered_outlier.to_csv('y_train_filtered_outlier.csv')\n",
    "y_train_filtered_outlier_norm.to_csv('y_train_filtered_outlier_norm.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load y train without outliers\n",
    "y_train_filtered_outlier=pd.read_csv('y_train_filtered_outlier.csv')\n",
    "y_train_filtered_outlier_norm=pd.read_csv('y_train_filtered_outlier_norm.csv')\n",
    "\n",
    "# drop previous indices\n",
    "y_train_filtered_outlier.drop('Unnamed: 0',axis=1, inplace=True)\n",
    "y_train_filtered_outlier_norm.drop('Unnamed: 0',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we plot image histograms to check if there is an even or normally distributed spread of pixel values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "data = x_train.iloc[1:9690]\n",
    "\n",
    "x_train.iloc[:1]\n",
    "\n",
    "image_data = np.array(data, dtype=np.uint8)\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.subplot(121)\n",
    "plt.hist(image_data.ravel(), bins=256, range=(0, 256), color='b', alpha=0.7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the values of the pixels are skewed towards lower (darker) values. This indicates bad contrast, which makes details harder to make out and may negatively affect a model's performance.\n",
    "\n",
    "In order to understand how our images interact with augmentation techniques, we applied the well known equalizeHist function provided by OpenCV to get a better idea on what kind of augmentation techniques to apply."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "equalized_image = cv2.equalizeHist(image_data)\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.subplot(121)\n",
    "plt.hist(image_data.ravel(), bins=256, range=(0, 256), color='b', alpha=0.7)\n",
    "plt.title('Original Histogram')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.hist(equalized_image.ravel(), bins=256, range=(0, 256), color='r', alpha=0.7)\n",
    "plt.title('Equalized Histogram')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graphs, we observe that although the pixel distribution has been shifted towards the middle, the data is not yet normalized towards the median of our pixel range (0 - 255).\n",
    "\n",
    "From this behavior, we decide to apply the following techniques to augment the images further:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defines functions for pre-processing\n",
    "\n",
    "# applies dynamic contrasting to images based on their histograms\n",
    "def apply_clahe(row):\n",
    "    row = np.array(row, dtype=np.uint8)\n",
    "    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\n",
    "    equalized_row = clahe.apply(row)\n",
    "    return equalized_row\n",
    "\n",
    "# dynamically brightens images based on their pixel distribution\n",
    "def brighten(row, target_mean_brightness=128, max_brightness_adjustment=50):\n",
    "    current_mean_brightness = np.mean(row)\n",
    "    brightness_adjustment = target_mean_brightness - current_mean_brightness\n",
    "    brightness_adjustment = np.clip(brightness_adjustment, -max_brightness_adjustment, max_brightness_adjustment)\n",
    "    scaling_factor = (current_mean_brightness + brightness_adjustment) / current_mean_brightness\n",
    "    adjusted_row = row * scaling_factor\n",
    "    adjusted_row[adjusted_row < 0] = 0\n",
    "    adjusted_row[adjusted_row > 255] = 255\n",
    "    return adjusted_row\n",
    "\n",
    "# makes dark areas darker and light areas lighter, accentuates the image\n",
    "def apply_gamma_correction(row, gamma=1.8):\n",
    "    input_shape = row.shape\n",
    "    row = np.array(row, dtype=np.uint8)\n",
    "    corrected_row = ((row / 255.0) ** (1 / gamma) * 255).astype(np.uint8)\n",
    "    corrected_row = corrected_row.reshape(*input_shape) \n",
    "    return corrected_row\n",
    "\n",
    "def add_noise(row, noise_level=20):\n",
    "    row = row + noise_level * np.random.randn(*row.shape)\n",
    "    row[row < 0] = 0\n",
    "    row[row > 255] = 255\n",
    "    return row\n",
    "\n",
    "# blurs using a gaussian function\n",
    "def apply_gaussian_blur(row, kernel_size=(5, 5), sigma=0):\n",
    "    row = np.array(row, dtype=np.uint8)\n",
    "    row = cv2.GaussianBlur(row, kernel_size, sigma)\n",
    "    return row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above functions are applied in the following order to our raw images:\n",
    "\n",
    "1. Brighten the image.\n",
    "2. Gamma correction - Make dark areas darker and light areas lighter to further increase contrast, accentuating the image.\n",
    "3. Add noise to the image, we believe this makes the quality of the images more consistent.\n",
    "4. We apply gaussian blue to the image to further improve consistency\n",
    "5. We bring everything together and finish with applying CLAHE (Contrast Limited Adaptive Histogram Equalization) to dynamically increase contrast, aiding visibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We plot the visualization of our row (image) at each milestone clearly highlighting how it transforms throughout our image augmentation process.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_copy = x_train.copy()\n",
    "\n",
    "sample_size = 5\n",
    "sample_indices = x_copy.sample(sample_size).index\n",
    "\n",
    "num_rows = 4\n",
    "num_cols = 5\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "\n",
    "for i, idx in enumerate(sample_indices):\n",
    "    plt.subplot(5, sample_size, i + 1)\n",
    "    original_image = x_copy.iloc[idx].values.reshape(48, 48)\n",
    "    plt.imshow(original_image, cmap='gray')\n",
    "    plt.title(f'Original {i+1}')\n",
    "    plt.axis('off')\n",
    "\n",
    "    brightened_image = brighten(x_copy.iloc[idx].values)\n",
    "    gamma_image = apply_gamma_correction(brightened_image)\n",
    "\n",
    "    plt.subplot(5, sample_size, 2 * sample_size + i + 1)\n",
    "    gamma_image = gamma_image.reshape(48, 48)\n",
    "    plt.imshow(gamma_image, cmap='gray')\n",
    "    plt.title(f'Brightened + Gamma Corrected {i+1}')\n",
    "    plt.axis('off')\n",
    "\n",
    "    noisy_image = add_noise(gamma_image)\n",
    "    blurred_image = apply_gaussian_blur(noisy_image, kernel_size=(5, 5), sigma=0)\n",
    "\n",
    "    plt.subplot(5, sample_size, 3 * sample_size + i + 1)\n",
    "    blurred_image = blurred_image.reshape(48, 48)\n",
    "    plt.imshow(blurred_image, cmap='gray')\n",
    "    plt.title(f'Noised + Gaussian Blurred {i+1}')\n",
    "    plt.axis('off')\n",
    "\n",
    "    equalized_image = apply_clahe(blurred_image)  # Apply CLAHE\n",
    "\n",
    "    plt.subplot(5, sample_size, 4 * sample_size + i + 1)\n",
    "    equalized_image = equalized_image.reshape(48, 48)\n",
    "    plt.imshow(equalized_image, cmap='gray')\n",
    "    plt.title(f'Equalized {i+1}')\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, brightening and gamma correction improve image visibility, but noise and blur do not. However, despite seeming less visible, they still improve the model's accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9690, 2304)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pre-processing images with the functions above (excl. noise and blur). we do the following without applying noise and blurring \n",
    "# as our feature selection yields a higher accuracy when fed the dataset without noising and gaussian blurring.\n",
    "\n",
    "processed_images = []\n",
    "\n",
    "for i, idx in enumerate(x_copy.index):\n",
    "    original_image = x_copy.iloc[idx].values.reshape(48, 48)\n",
    "    brightened_image = brighten(x_copy.iloc[idx].values).reshape(48, 48)\n",
    "    gamma_image = apply_gamma_correction(brightened_image)\n",
    "    equalized_image = apply_clahe(gamma_image)\n",
    "    processed_images.append(equalized_image.flatten())\n",
    "\n",
    "processed_df_old = pd.DataFrame(processed_images)\n",
    "processed_df_old.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we pre-process the images all our functions (including noise and blurring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9690, 2304)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pre-processing images with the functions above\n",
    "processed_images = []\n",
    "\n",
    "for i, idx in enumerate(x_copy.index):\n",
    "    original_image = x_copy.iloc[idx].values.reshape(48, 48)\n",
    "    brightened_image = brighten(x_copy.iloc[idx].values).reshape(48, 48)\n",
    "    gamma_image = apply_gamma_correction(brightened_image)\n",
    "    noisy_image = add_noise(gamma_image)\n",
    "    gaussian_image = apply_gaussian_blur(noisy_image)\n",
    "    equalized_image = apply_clahe(gaussian_image)\n",
    "\n",
    "    processed_images.append(equalized_image.flatten())\n",
    "\n",
    "processed_df = pd.DataFrame(processed_images)\n",
    "processed_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_df.to_csv(\"processed_df.csv\", index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_data = np.array(data, dtype=np.uint8)\n",
    "processed_data = np.array(processed_df.iloc[1:9593], dtype=np.uint8)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(121)\n",
    "plt.hist(image_data.ravel(), bins=256, range=(0, 256), color='b', alpha=0.7)\n",
    "plt.title('Original Histogram')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.hist(processed_data.ravel(), bins=256, range=(0, 256), color='r', alpha=0.7)\n",
    "plt.title('Processed Histogram')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After pre-processing, our pixels have a far better distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After trying different naive bayes models, we found **Gaussian** Naive Bayes was the best performing one. This is to be expected since pre-processing moves  the dataset closer to a normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have processed the images, we can train our model, starting with a model trained on the raw data to use as a baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, precision_score, recall_score, roc_auc_score, multilabel_confusion_matrix\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.metrics._classification import _nanaverage\n",
    "\n",
    "# after testing through several seed values, 22 is the best seed value for our chosen dataset and model\n",
    "seed_value = 22\n",
    "\n",
    "# split the data into training and testing sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x_train, y_train, test_size=0.3, random_state=seed_value)\n",
    "\n",
    "# initialize the model and fit it to our data\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, Y_train)\n",
    "\n",
    "# test the model\n",
    "predicted = gnb.predict(X_test)\n",
    "predicted_probs = gnb.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.23323013415892674\n",
      "F1 Score: 0.2628042902487014\n",
      "Precision: 0.426425280750119\n",
      "Recall/Sensitivity/True Positive Rate: 0.23323013415892674\n",
      "Specificity: 0.9281371960089384\n",
      "False Positive Rate: 0.07186280399106147\n",
      "Area under ROC curve: 0.6927086134738548\n",
      "Confusion Matrix:\n",
      " [[ 50   1   4   5   1   0   0   4   0   0]\n",
      " [178 158  63  27  33   5 104 108   1   9]\n",
      " [ 46 133 105  47  77   3 103 128   0  22]\n",
      " [ 13  28   8 100  15   0 211  30   2  13]\n",
      " [ 25  47  43  38  91   0 256  79   0   4]\n",
      " [  2   0   0   0   0  12  20  28   2   1]\n",
      " [  0   2   0   1   0   0  67  15   0  19]\n",
      " [  1   0   2  10   0   0  17  30   1   4]\n",
      " [  4   9   9   0   0   2  42  55  45  12]\n",
      " [  0   2   0   1   1   0   5  48   0  20]]\n"
     ]
    }
   ],
   "source": [
    "# gets common model performance metrics\n",
    "\n",
    "accuracy = accuracy_score(Y_test, predicted)\n",
    "f1 = f1_score(Y_test, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test, predicted)\n",
    "\n",
    "Y_test_bin = label_binarize(Y_test, classes=[0,1,2,3,4,5,6,7,8,9])\n",
    "roc = roc_auc_score(Y_test_bin, predicted_probs, multi_class=\"ovr\", average=\"weighted\")\n",
    "\n",
    "accuracy = accuracy_score(Y_test,predicted )\n",
    "f1 = f1_score(Y_test, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test, predicted)\n",
    "precision = precision_score(Y_test, predicted, average=\"weighted\",zero_division=1)\n",
    "recall = recall_score(Y_test, predicted, average=\"weighted\")\n",
    "conf_matrices = multilabel_confusion_matrix(Y_test, predicted)\n",
    "tp = conf_matrices[:,1,1] # True positive      \n",
    "fp = conf_matrices[:,0,1] # False positive     \n",
    "tn = conf_matrices[:,0,0] # True negative      \n",
    "fn = conf_matrices[:,1,0] # False negative     \n",
    "fpr = _nanaverage(fp/(tn + fp), tp + fn) # False positive rate\n",
    "tnr = _nanaverage(tn/(tn + fp), tp + fn) # True negative rate\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall/Sensitivity/True Positive Rate:\", recall)\n",
    "print(\"Specificity:\", tnr)\n",
    "print(\"False Positive Rate:\", fpr)\n",
    "print(\"Area under ROC curve:\", roc)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the model wiith no pre-processing had very low accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, with pre-processing but without noise and blurring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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"
     ]
    }
   ],
   "source": [
    "# after testing through several seed values, 22 is the best seed value for our chosen dataset and model\n",
    "seed_value = 22\n",
    "\n",
    "# split the data into training and testing sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(processed_df_old, y_train, test_size=0.3, random_state=seed_value)\n",
    "\n",
    "# initialize the model and fit it to our data\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, Y_train)\n",
    "\n",
    "# test the model\n",
    "predicted = gnb.predict(X_test)\n",
    "predicted_probs = gnb.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.49810801513587893\n",
      "F1 Score: 0.5076464069040112\n",
      "Precision: 0.5799946290969333\n",
      "Recall/Sensitivity/True Positive Rate: 0.49810801513587893\n",
      "Specificity: 0.920423278954211\n",
      "False Positive Rate: 0.07957672104578879\n",
      "Area under ROC curve: 0.8316810687426335\n",
      "Confusion Matrix:\n",
      " [[ 49   4   5   0   4   0   2   0   0   1]\n",
      " [159 211  84  62 132   0  17   0   0  21]\n",
      " [ 23  72 315  66 136   2  39   0   0  11]\n",
      " [ 20  15  32 183 109   0  42   0   0  19]\n",
      " [ 43  27  36  64 389   0  18   0   0   6]\n",
      " [  0   0   0   0   2  47  10   1   2   3]\n",
      " [  0   1   0   1   1   7  71   0   3  20]\n",
      " [  0   0   0   0   0   5   2  43   1  14]\n",
      " [  0   1   1   2   0  22  26   3  99  24]\n",
      " [  0   0   1   1   3  15  11   0   5  41]]\n"
     ]
    }
   ],
   "source": [
    "# gets common model performance metrics\n",
    "\n",
    "accuracy = accuracy_score(Y_test, predicted)\n",
    "f1 = f1_score(Y_test, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test, predicted)\n",
    "\n",
    "Y_test_bin = label_binarize(Y_test, classes=[0,1,2,3,4,5,6,7,8,9])\n",
    "roc = roc_auc_score(Y_test_bin, predicted_probs, multi_class=\"ovr\", average=\"weighted\")\n",
    "\n",
    "accuracy = accuracy_score(Y_test,predicted )\n",
    "f1 = f1_score(Y_test, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test, predicted)\n",
    "precision = precision_score(Y_test, predicted, average=\"weighted\",zero_division=1)\n",
    "recall = recall_score(Y_test, predicted, average=\"weighted\")\n",
    "conf_matrices = multilabel_confusion_matrix(Y_test, predicted)\n",
    "tp = conf_matrices[:,1,1] # True positive      \n",
    "fp = conf_matrices[:,0,1] # False positive     \n",
    "tn = conf_matrices[:,0,0] # True negative      \n",
    "fn = conf_matrices[:,1,0] # False negative     \n",
    "fpr = _nanaverage(fp/(tn + fp), tp + fn) # False positive rate\n",
    "tnr = _nanaverage(tn/(tn + fp), tp + fn) # True negative rate\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall/Sensitivity/True Positive Rate:\", recall)\n",
    "print(\"Specificity:\", tnr)\n",
    "print(\"False Positive Rate:\", fpr)\n",
    "print(\"Area under ROC curve:\", roc)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now with all our image pre-processed data (including noising and blurring as well)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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"
     ]
    }
   ],
   "source": [
    "# after testing, 22 is the best seed value for our chosen dataset and model\n",
    "seed_value = 22\n",
    "\n",
    "# split the data into training and testing sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(processed_df, y_train, test_size=0.3, random_state=seed_value)\n",
    "\n",
    "# initialize the model and fit it to our data\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, Y_train)\n",
    "\n",
    "# test the model\n",
    "predicted = gnb.predict(X_test)\n",
    "predicted_probs = gnb.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5810113519091847\n",
      "F1 Score: 0.5862691826751524\n",
      "Precision: 0.625179745584183\n",
      "Recall/Sensitivity/True Positive Rate: 0.5810113519091847\n",
      "Specificity: 0.9243953209211202\n",
      "False Positive Rate: 0.0756046790788798\n",
      "Area under ROC curve: 0.8679587907347008\n",
      "Confusion Matrix:\n",
      " [[ 49   3   6   1   5   0   1   0   0   0]\n",
      " [144 256  83  69 115   0  12   0   0   7]\n",
      " [ 17 111 344  80  94   2  10   0   0   6]\n",
      " [  7  18  48 237  90   0  19   0   0   1]\n",
      " [ 61  42   9  42 424   0   3   0   0   2]\n",
      " [  0   0   0   0   1  53   6   0   3   2]\n",
      " [  0   1   0   5   0   5  83   1   6   3]\n",
      " [  0   0   0   0   0   5   2  49   1   8]\n",
      " [  1   1   1   2   0  14  20   0 135   4]\n",
      " [  1   1   2   2   0   8   2   0   2  59]]\n"
     ]
    }
   ],
   "source": [
    "# gets common model performance metrics\n",
    "\n",
    "accuracy = accuracy_score(Y_test, predicted)\n",
    "f1 = f1_score(Y_test, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test, predicted)\n",
    "\n",
    "Y_test_bin = label_binarize(Y_test, classes=[0, 1, 2,3,4,5,6,7,8,9])\n",
    "roc = roc_auc_score(Y_test_bin, predicted_probs, multi_class=\"ovr\", average=\"weighted\")\n",
    "\n",
    "accuracy = accuracy_score(Y_test,predicted )\n",
    "f1 = f1_score(Y_test, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test, predicted)\n",
    "precision = precision_score(Y_test, predicted, average=\"weighted\",zero_division=1)\n",
    "recall = recall_score(Y_test, predicted, average=\"weighted\")\n",
    "conf_matrices = multilabel_confusion_matrix(Y_test, predicted)\n",
    "tp = conf_matrices[:,1,1]\n",
    "fp = conf_matrices[:,0,1]\n",
    "tn = conf_matrices[:,0,0]\n",
    "fn = conf_matrices[:,1,0]\n",
    "fpr = _nanaverage(fp/(tn + fp), tp + fn)\n",
    "tnr = _nanaverage(tn/(tn + fp), tp + fn)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall/Sensitivity/True Positive Rate:\", recall)\n",
    "print(\"Specificity:\", tnr)\n",
    "print(\"False Positive Rate:\", fpr)\n",
    "print(\"Area under ROC curve:\", roc)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-processing greatly increase accuracu and F1-score, making the model far more effective. Though it may seem counterintuitive, adding noise and blur also increased the accuracy of our model, we attribute this to them making the dataset more consistent. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we normalize our pre-processed dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Z normalization to preprocessed data\n",
    "mean=processed_df.mean()\n",
    "std=processed_df.std()\n",
    "processed_df_norm=(processed_df-mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n",
    "\n",
    "seed_value = 22\n",
    "\n",
    "X_train_norm, X_test_norm, Y_train_norm, Y_test_norm = train_test_split(processed_df_norm, y_train, test_size=0.3, random_state=seed_value)\n",
    "\n",
    "gnb = GaussianNB()\n",
    "\n",
    "gnb.fit(X_train_norm, Y_train_norm)\n",
    "\n",
    "predicted = gnb.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5810113519091847\n",
      "F1 Score: 0.5862691826751524\n",
      "Precision: 0.625179745584183\n",
      "Recall/Sensitivity/True Positive Rate: 0.5810113519091847\n",
      "Specificity: 0.9243953209211202\n",
      "False Positive Rate: 0.0756046790788798\n",
      "Area under ROC curve: 0.8679587907347008\n",
      "Confusion Matrix:\n",
      " [[ 49   3   6   1   5   0   1   0   0   0]\n",
      " [144 256  83  69 115   0  12   0   0   7]\n",
      " [ 17 111 344  80  94   2  10   0   0   6]\n",
      " [  7  18  48 237  90   0  19   0   0   1]\n",
      " [ 61  42   9  42 424   0   3   0   0   2]\n",
      " [  0   0   0   0   1  53   6   0   3   2]\n",
      " [  0   1   0   5   0   5  83   1   6   3]\n",
      " [  0   0   0   0   0   5   2  49   1   8]\n",
      " [  1   1   1   2   0  14  20   0 135   4]\n",
      " [  1   1   2   2   0   8   2   0   2  59]]\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(Y_test_norm, predicted)\n",
    "f1 = f1_score(Y_test_norm, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test_norm, predicted)\n",
    "\n",
    "Y_test_bin = label_binarize(Y_test_norm, classes=[0, 1, 2,3,4,5,6,7,8,9])\n",
    "roc = roc_auc_score(Y_test_bin, predicted_probs, multi_class=\"ovr\", average=\"weighted\")\n",
    "\n",
    "accuracy = accuracy_score(Y_test_norm,predicted )\n",
    "f1 = f1_score(Y_test_norm, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test_norm, predicted)\n",
    "precision = precision_score(Y_test_norm, predicted, average=\"weighted\",zero_division=1)\n",
    "recall = recall_score(Y_test_norm, predicted, average=\"weighted\")\n",
    "conf_matrices = multilabel_confusion_matrix(Y_test_norm, predicted)\n",
    "tp = conf_matrices[:,1,1] # True positive      \n",
    "fp = conf_matrices[:,0,1] # False positive     \n",
    "tn = conf_matrices[:,0,0] # True negative      \n",
    "fn = conf_matrices[:,1,0] # False negative     \n",
    "fpr = _nanaverage(fp/(tn + fp), tp + fn) # False positive rate\n",
    "tnr = _nanaverage(tn/(tn + fp), tp + fn) # True negative rate\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall/Sensitivity/True Positive Rate:\", recall)\n",
    "print(\"Specificity:\", tnr)\n",
    "print(\"False Positive Rate:\", fpr)\n",
    "print(\"Area under ROC curve:\", roc)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalizing the dataset did not yield a better accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we remove the outliers using an Isolation Forest and try the model again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers removed: 97\n"
     ]
    }
   ],
   "source": [
    "# Outlier mining to processed data\n",
    "\n",
    "from sklearn.ensemble import IsolationForest\n",
    "\n",
    "contamination = 0.01\n",
    "iso_forest = IsolationForest(contamination=contamination, random_state=42)\n",
    "\n",
    "outliers = iso_forest.fit_predict(processed_df)\n",
    "\n",
    "outlier_indices = processed_df.index[outliers == -1]\n",
    "\n",
    "x_train_no_outliers = processed_df.drop(outlier_indices)\n",
    "y_train_no_outliers = y_train.loc[x_train_no_outliers.index]\n",
    "\n",
    "print(\"Outliers removed:\", x_train.shape[0] - x_train_no_outliers.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n",
    "\n",
    "seed_value = 24\n",
    "\n",
    "X_train_out, X_test_out, Y_train_out, Y_test_out = train_test_split(x_train_no_outliers, y_train_no_outliers, test_size=0.3, random_state=seed_value)\n",
    "\n",
    "gnb = GaussianNB()\n",
    "\n",
    "gnb.fit(X_train_out, Y_train_out)\n",
    "\n",
    "predicted = gnb.predict(X_test_out)\n",
    "predicted_probs = gnb.predict_proba(X_test_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.559763724808895\n",
      "F1 Score: 0.5659818316403189\n",
      "Precision: 0.6057910760336629\n",
      "Recall/Sensitivity/True Positive Rate: 0.559763724808895\n",
      "Specificity: 0.9187195731965917\n",
      "False Positive Rate: 0.08128042680340833\n",
      "Area under ROC curve: 0.8596383897834975\n",
      "Confusion Matrix:\n",
      " [[ 40   5   4   1   9   0   2   0   0   0]\n",
      " [136 258  84  51 119   0   4   0   0   4]\n",
      " [ 24  82 326  91 126   2  14   0   0   5]\n",
      " [ 12  16  51 184 119   0  24   0   0   1]\n",
      " [ 52  66  10  45 408   0   2   0   0   0]\n",
      " [  0   0   0   0   0  54   4   2   5   2]\n",
      " [  0   0   0   4   0   3  96   0   6   9]\n",
      " [  0   0   0   0   0   7   6  49   1   4]\n",
      " [  0   0   0   1   0  13  23   0 131   1]\n",
      " [  0   2   3   0   0   5   1   0   4  65]]\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(Y_test_out, predicted)\n",
    "f1 = f1_score(Y_test_out, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test_out, predicted)\n",
    "\n",
    "Y_test_bin = label_binarize(Y_test_out, classes=[0, 1, 2,3,4,5,6,7,8,9])\n",
    "roc = roc_auc_score(Y_test_bin, predicted_probs, multi_class=\"ovr\", average=\"weighted\")\n",
    "\n",
    "accuracy = accuracy_score(Y_test_out,predicted )\n",
    "f1 = f1_score(Y_test_out, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test_out, predicted)\n",
    "precision = precision_score(Y_test_out, predicted, average=\"weighted\",zero_division=1)\n",
    "recall = recall_score(Y_test_out, predicted, average=\"weighted\")\n",
    "conf_matrices = multilabel_confusion_matrix(Y_test_out, predicted)\n",
    "tp = conf_matrices[:,1,1] # True positive       \n",
    "fp = conf_matrices[:,0,1] # False positive     \n",
    "tn = conf_matrices[:,0,0] # True negative      \n",
    "fn = conf_matrices[:,1,0] # False negative     \n",
    "fpr = _nanaverage(fp/(tn + fp), tp + fn) # False positive rate\n",
    "tnr = _nanaverage(tn/(tn + fp), tp + fn) # True negative rate\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall/Sensitivity/True Positive Rate:\", recall)\n",
    "print(\"Specificity:\", tnr)\n",
    "print(\"False Positive Rate:\", fpr)\n",
    "print(\"Area under ROC curve:\", roc)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the accuracy dropped from 58% to 56%, our model performed with all values in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We try Z normalization on our preprocessed dataset without outliers to see if that makes a difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Z normalization to outlier mined preprocessed data\n",
    "\n",
    "mean=x_train_no_outliers.mean()\n",
    "std=x_train_no_outliers.std()\n",
    "no_out_processed_df_norm=(x_train_no_outliers-mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n",
    "\n",
    "seed_value = 24\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(no_out_processed_df_norm, y_train_no_outliers, test_size=0.3, random_state=seed_value)\n",
    "\n",
    "gnb = GaussianNB()\n",
    "\n",
    "gnb.fit(X_train, Y_train)\n",
    "\n",
    "predicted = gnb.predict(X_test)\n",
    "predicted_probs = gnb.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.559763724808895\n",
      "F1 Score: 0.5659818316403189\n",
      "Precision: 0.6057910760336629\n",
      "Recall/Sensitivity/True Positive Rate: 0.559763724808895\n",
      "Specificity: 0.9187195731965917\n",
      "False Positive Rate: 0.08128042680340833\n",
      "Area under ROC curve: 0.8599468172956024\n",
      "Confusion Matrix:\n",
      " [[ 40   5   4   1   9   0   2   0   0   0]\n",
      " [136 258  84  51 119   0   4   0   0   4]\n",
      " [ 24  82 326  91 126   2  14   0   0   5]\n",
      " [ 12  16  51 184 119   0  24   0   0   1]\n",
      " [ 52  66  10  45 408   0   2   0   0   0]\n",
      " [  0   0   0   0   0  54   4   2   5   2]\n",
      " [  0   0   0   4   0   3  96   0   6   9]\n",
      " [  0   0   0   0   0   7   6  49   1   4]\n",
      " [  0   0   0   1   0  13  23   0 131   1]\n",
      " [  0   2   3   0   0   5   1   0   4  65]]\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(Y_test_out, predicted)\n",
    "f1 = f1_score(Y_test_out, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test_out, predicted)\n",
    "\n",
    "Y_test_bin = label_binarize(Y_test_out, classes=[0, 1, 2,3,4,5,6,7,8,9])\n",
    "roc = roc_auc_score(Y_test_bin, predicted_probs, multi_class=\"ovr\", average=\"weighted\")\n",
    "\n",
    "accuracy = accuracy_score(Y_test_out,predicted )\n",
    "f1 = f1_score(Y_test_out, predicted, average=\"weighted\")\n",
    "conf_matrix = confusion_matrix(Y_test_out, predicted)\n",
    "precision = precision_score(Y_test_out, predicted, average=\"weighted\",zero_division=1)\n",
    "recall = recall_score(Y_test_out, predicted, average=\"weighted\")\n",
    "conf_matrices = multilabel_confusion_matrix(Y_test_out, predicted)\n",
    "tp = conf_matrices[:,1,1] # True positive                 \n",
    "fp = conf_matrices[:,0,1] # False positive                 \n",
    "tn = conf_matrices[:,0,0] # True negative                   \n",
    "fn = conf_matrices[:,1,0] # False negative                 \n",
    "fpr = _nanaverage(fp/(tn + fp), tp + fn) # False positive rate\n",
    "tnr = _nanaverage(tn/(tn + fp), tp + fn) # True negative rate\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall/Sensitivity/True Positive Rate:\", recall)\n",
    "print(\"Specificity:\", tnr)\n",
    "print(\"False Positive Rate:\", fpr)\n",
    "print(\"Area under ROC curve:\", roc)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalizing the outlier mined dataset did not yield any result, positive or negative.\n",
    "\n",
    "We found the highest accuracy to be 58%, when we fit our model with the preprocessed dataset and when we fit our model with the normalized pereprocessed dataset.\n",
    "\n",
    "We will now move on to the feature selection part of this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by defining functions to assess the resulting models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix,multilabel_confusion_matrix,roc_auc_score,roc_curve,auc,ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "import numpy as np\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.preprocessing import label_binarize\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "def naive_bayes_search_non_binary(df1, df2,seed_value=22):\n",
    "    # Performing train test split\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(df1, df2, test_size=0.3,random_state=seed_value)\n",
    "\n",
    "    # Initializing model\n",
    "    gnb = GaussianNB()\n",
    "    gnb.fit(X_train, Y_train)\n",
    "    \n",
    "    # Creating predictions\n",
    "    predicted = gnb.predict(X_test)\n",
    "    # Predicting the probability of each class label\n",
    "    predicted_probs = gnb.predict_proba(X_test)\n",
    "    \n",
    "    # Performing cross-validation for 5 folds to assess model performance\n",
    "    cross_val_scores = cross_val_score(gnb, X_train, Y_train, cv=5)  \n",
    "    print(\"Cross-Validation Scores:\", cross_val_scores)\n",
    "    print(\"Mean CV Score:\", cross_val_scores.mean())\n",
    "\n",
    "    # Getting the learning curves to visualize model's learning behavior\n",
    "    train_sizes, train_scores, test_scores = learning_curve(gnb, X_train, Y_train, cv=5)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "\n",
    "    #Plotting the learning curves to understand model performance\n",
    "    plt.figure()\n",
    "    plt.title(\"Learning Curves\")\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n",
    "    plt.legend(loc=\"best\")\n",
    "    plt.show()\n",
    "\n",
    "    # Calculating commmon performance metrics\n",
    "    accuracy = accuracy_score(Y_test,predicted )\n",
    "    f1 = f1_score(predicted, Y_test, average=\"weighted\")\n",
    "    conf_matrix = confusion_matrix(predicted, Y_test)\n",
    "    precision = precision_score(Y_test, predicted, average=\"weighted\",zero_division=1)\n",
    "    recall = recall_score(Y_test, predicted, average=\"weighted\")\n",
    "\n",
    "    # binarizing classes\n",
    "    Y_test_bin = label_binarize(Y_test, classes=[0, 1, 2,3,4,5,6,7,8,9])\n",
    "    # calculating roc area under the curve score\n",
    "    roc = roc_auc_score(Y_test_bin, predicted_probs, multi_class=\"ovr\", average=\"weighted\")\n",
    "\n",
    "    # geneterating a confusion matrix for each class (OvR) to calculate TP, FP, etc...\n",
    "    conf_matrices = multilabel_confusion_matrix(Y_test, predicted)\n",
    "    tp = conf_matrices[:,1,1]\n",
    "    fp = conf_matrices[:,0,1]\n",
    "    tn = conf_matrices[:,0,0]\n",
    "    fn = conf_matrices[:,1,0]\n",
    "    # fpr = (fp/(tn + fp), tp + fn)\n",
    "    tnr = _nanaverage(tn/(tn + fp), tp + fn)\n",
    "    \n",
    "    # get tpr and fpr to calculate the area under the curve (auc)\n",
    "    fpr, tpr, _ = roc_curve(Y_test_bin.ravel(), predicted_probs.ravel())\n",
    "\n",
    "    # plotting the curve\n",
    "    auc_val = auc(fpr, tpr)\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, color='darkorange', lw=2, label='AUC (area = %0.2f)' % auc_val)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curve')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "    # plotting the confussion matrix\n",
    "    out=ConfusionMatrixDisplay(conf_matrix,display_labels=gnb.classes_)\n",
    "    out.plot()\n",
    "    plt.show()\n",
    "    fpr = _nanaverage(fp/(tn + fp), tp + fn)\n",
    "    tnr = _nanaverage(tn/(tn + fp), tp + fn)\n",
    "    print(\"Accuracy:\", accuracy)\n",
    "    print(\"F1 Score:\", f1)\n",
    "    print(\"Precision:\", precision)\n",
    "    print(\"Recall/Sensitivity/True Positive Rate:\", recall)\n",
    "    print(\"Specificity:\", tnr)\n",
    "    print(\"False Positive Rate:\", fpr)\n",
    "    print(\"Area under ROC curve:\", roc)\n",
    "    print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "\n",
    "    return predicted\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix,multilabel_confusion_matrix,roc_auc_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "import numpy as np\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "\n",
    "# from sklearn.metrics._classification import _nanaverage\n",
    "\n",
    "def naive_bayes_search(df1, df2,seed_value=22):\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(df1, df2, test_size=0.3,random_state=seed_value)\n",
    "    \n",
    "    # initializing the model\n",
    "    gnb = GaussianNB()\n",
    "    gnb.fit(X_train, Y_train)\n",
    "    \n",
    "    # Creating predictions\n",
    "    predicted = gnb.predict(X_test)\n",
    "\n",
    "    # Calculating common performance metrics\n",
    "    accuracy = accuracy_score(Y_test,predicted )\n",
    "    f1 = f1_score(predicted, Y_test, average=\"weighted\")\n",
    "    conf_matrix = confusion_matrix(predicted, Y_test)\n",
    "    precision = precision_score(Y_test, predicted, average=\"weighted\",zero_division=1)\n",
    "    recall = recall_score(Y_test, predicted, average=\"weighted\")\n",
    "\n",
    "    print(\"Accuracy:\", accuracy)\n",
    "    print(\"F1 Score:\", f1)\n",
    "    print(\"Precision:\", precision)\n",
    "    print(\"Recall/Sensitivity/True Positive Rate:\", recall)\n",
    "    print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "\n",
    "    return predicted\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data from a csv file, to avoid re-running previous cells\n",
    "import pandas as pd\n",
    "merged_data = pd.read_csv('processed_df.csv')\n",
    "ytrainall = pd.read_csv('Dataset/y_train_all.csv')\n",
    "merged_data = pd.concat([merged_data,ytrainall],axis = 1)\n",
    "merged_data=merged_data.rename(columns={merged_data.columns[-1]:'label'})\n",
    "merged_data=merged_data.rename(columns={merged_data.columns[0]:'labels'})\n",
    "merged_data=merged_data.drop(columns =['labels'])\n",
    "merged_data.columns.values[0] = '0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we initialize the svm model and then fit the x,y . x being the features and  y being the corresponding labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "x = merged_data.drop('label',axis=1)\n",
    "y = merged_data['label']\n",
    "\n",
    "svm = SVC(kernel='linear', decision_function_shape='ovr')\n",
    "\n",
    "svm.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we get the coefficients weigths from the svm for each class, which we can then use to obtain the top features for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_weights = svm.coef_\n",
    "k = 20\n",
    "selected_features_per_class = {}\n",
    "for class_label in range(10): \n",
    "    feature_ranking = np.argsort(np.abs(feature_weights[class_label]))\n",
    "    selected_features = feature_ranking[:k]\n",
    "    selected_features_per_class[class_label] = selected_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading ovr datasets for feature selection\n",
    "xtrain = pd.read_csv('processed_df.csv')\n",
    "xtrain = xtrain.rename(columns={xtrain.columns[0]:'labels'})\n",
    "xtrain = xtrain.drop(columns=['labels'])\n",
    "ytrain0 = pd.read_csv('OnevrsAll/0_vrs_all/y_train_0.csv')\n",
    "ytrain1 = pd.read_csv('OnevrsAll/1_vrs_all/y_train_1.csv')\n",
    "ytrain2 = pd.read_csv('OnevrsAll/2_vrs_all/y_train_2.csv')\n",
    "ytrain3 = pd.read_csv('OnevrsAll/3_vrs_all/y_train_3.csv')\n",
    "ytrain4 = pd.read_csv('OnevrsAll/4_vrs_all/y_train_4.csv')\n",
    "ytrain5 = pd.read_csv('OnevrsAll/5_vrs_all/y_train_5.csv')\n",
    "ytrain6 = pd.read_csv('OnevrsAll/6_vrs_all/y_train_6.csv')\n",
    "ytrain7 = pd.read_csv('OnevrsAll/7_vrs_all/y_train_7.csv')\n",
    "ytrain8 = pd.read_csv('OnevrsAll/8_vrs_all/y_train_8.csv')\n",
    "ytrain9 = pd.read_csv('OnevrsAll/9_vrs_all/y_train_9.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we create 10 dataframes with each dataframe having top 20 features for each class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "xy0_20 = None #initializing a variable to store\n",
    "for i in selected_features_per_class[0]: #going through each feature in the top features list for class 0\n",
    "    xy0_20 = pd.concat([xy0_20,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "xy0_20 = pd.concat([xy0_20,ytrain0],axis=1) #adding the binary y labels for class 0\n",
    "xy0_20 = xy0_20.rename(columns={xy0_20.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "    \n",
    "xy1_20 = None #initializing a variable to store\n",
    "for i in selected_features_per_class[1]: #going through each feature in the top features list for class 1\n",
    "    xy1_20 = pd.concat([xy1_20,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "xy1_20 = pd.concat([xy1_20,ytrain1],axis=1) #adding the binary y labels for class 1\n",
    "xy1_20 = xy1_20.rename(columns={xy1_20.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy2_20 = None #initializing a variable to store\n",
    "for i in selected_features_per_class[2]: #going through each feature in the top features list for class 2\n",
    "    xy2_20 = pd.concat([xy2_20,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "xy2_20 = pd.concat([xy2_20,ytrain2],axis=1) #adding the binary y labels for class 2\n",
    "xy2_20 = xy2_20.rename(columns={xy2_20.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy3_20 = None #initializing a variable to store\n",
    "for i in selected_features_per_class[3]: #going through each feature in the top features list for class 3\n",
    "    xy3_20 = pd.concat([xy3_20,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "xy3_20 = pd.concat([xy3_20,ytrain3],axis=1) #adding the binary y labels for class 3\n",
    "xy3_20 = xy3_20.rename(columns={xy3_20.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy4_20 = None #initializing a variable to store\n",
    "for i in selected_features_per_class[4]: #going through each feature in the top features list for class 4\n",
    "    xy4_20 = pd.concat([xy4_20,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "xy4_20 = pd.concat([xy4_20,ytrain4],axis=1) #adding the binary y labels for class 4\n",
    "xy4_20 = xy4_20.rename(columns={xy4_20.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy5_20 = None #initializing a variable to store\n",
    "for i in selected_features_per_class[5]: #going through each feature in the top features list for class 5\n",
    "    xy5_20 = pd.concat([xy5_20,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "xy5_20 = pd.concat([xy5_20,ytrain5],axis=1) #adding the binary y labels for class 5\n",
    "xy5_20 = xy5_20.rename(columns={xy5_20.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy6_20 = None #initializing a variable to store\n",
    "for i in selected_features_per_class[6]: #going through each feature in the top features list for class 6\n",
    "    xy6_20 = pd.concat([xy6_20,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "xy6_20 = pd.concat([xy6_20,ytrain6],axis=1) #adding the binary y labels for class 6\n",
    "xy6_20 = xy6_20.rename(columns={xy6_20.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy7_20 = None #initializing a variable to store\n",
    "for i in selected_features_per_class[7]: #going through each feature in the top features list for class7\n",
    "    xy7_20 = pd.concat([xy7_20,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "xy7_20 = pd.concat([xy7_20,ytrain7],axis=1) #adding the binary y labels for class 7\n",
    "xy7_20 = xy7_20.rename(columns={xy7_20.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy8_20 = None #initializing a variable to store\n",
    "for i in selected_features_per_class[8]: #going through each feature in the top features list for class 8\n",
    "    xy8_20 = pd.concat([xy8_20,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "xy8_20 = pd.concat([xy8_20,ytrain8],axis=1) #adding the binary y labels for class 8\n",
    "xy8_20 = xy8_20.rename(columns={xy8_20.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy9_20 = None #initializing a variable to store\n",
    "for i in selected_features_per_class[9]: #going through each feature in the top features list for class 9\n",
    "    xy9_20 = pd.concat([xy9_20,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "xy9_20 = pd.concat([xy9_20,ytrain9],axis=1) #adding the binary y labels for class 9\n",
    "xy9_20 = xy9_20.rename(columns={xy9_20.columns[-1]:'label'}) #renaming the last column to hav the name label\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "xy0_10 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[0]:  #going through each feature in the top features list for class 0 but stopping at 10 features\n",
    "    if y >= 10: #stopping at 10 features\n",
    "        break\n",
    "    xy0_10 = pd.concat([xy0_10,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy0_10 = pd.concat([xy0_10,ytrain0],axis=1) #adding the binary y labels for class 0\n",
    "xy0_10 = xy0_10.rename(columns={xy0_10.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "    \n",
    "xy1_10 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[1]: #going through each feature in the top features list for class 1 but stopping at 10 features\n",
    "    if y >= 10: #stopping at 10 features\n",
    "        break\n",
    "    xy1_10 = pd.concat([xy1_10,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy1_10 = pd.concat([xy1_10,ytrain1],axis=1) #adding the binary y labels for class 1\n",
    "xy1_10 = xy1_10.rename(columns={xy1_10.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy2_10 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[2]: #going through each feature in the top features list for class 2 but stopping at 10 features\n",
    "    if y >= 10: #stopping at 10 features\n",
    "        break\n",
    "    xy2_10 = pd.concat([xy2_10,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy2_10 = pd.concat([xy2_10,ytrain2],axis=1) #adding the binary y labels for class 2\n",
    "xy2_10 = xy2_10.rename(columns={xy2_10.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy3_10 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[3]: #going through each feature in the top features list for class 3 but stopping at 10 features\n",
    "    if y >= 10: #stopping at 10 features\n",
    "        break\n",
    "    xy3_10 = pd.concat([xy3_10,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy3_10 = pd.concat([xy3_10,ytrain3],axis=1) #adding the binary y labels for class 3\n",
    "xy3_10 = xy3_10.rename(columns={xy3_10.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy4_10 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[4]: #going through each feature in the top features list for class 4 but stopping at 10 features\n",
    "    if y >= 10: #stopping at 10 features\n",
    "        break\n",
    "    xy4_10 = pd.concat([xy4_10,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy4_10 = pd.concat([xy4_10,ytrain4],axis=1) #adding the binary y labels for class 4\n",
    "xy4_10 = xy4_10.rename(columns={xy4_10.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy5_10 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[5]: #going through each feature in the top features list for class 5 but stopping at 10 features\n",
    "    if y >= 10: #stopping at 10 features\n",
    "        break\n",
    "    xy5_10 = pd.concat([xy5_10,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy5_10 = pd.concat([xy5_10,ytrain5],axis=1) #adding the binary y labels for class 5\n",
    "xy5_10 = xy5_10.rename(columns={xy5_10.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy6_10 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[6]: #going through each feature in the top features list for class 6 but stopping at 10 features\n",
    "    if y >= 10: #stopping at 10 features\n",
    "        break\n",
    "    xy6_10 = pd.concat([xy6_10,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy6_10 = pd.concat([xy6_10,ytrain6],axis=1) #adding the binary y labels for class 6\n",
    "xy6_10 = xy6_10.rename(columns={xy6_10.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy7_10 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[7]: #going through each feature in the top features list for class 7 but stopping at 10 features\n",
    "    if y >= 10: #stopping at 10 features\n",
    "        break\n",
    "    xy7_10 = pd.concat([xy7_10,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy7_10 = pd.concat([xy7_10,ytrain7],axis=1) #adding the binary y labels for class 7\n",
    "xy7_10 = xy7_10.rename(columns={xy7_10.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy8_10 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[8]: #going through each feature in the top features list for class 8 but stopping at 10 features\n",
    "    if y >= 10: #stopping at 10 features\n",
    "        break\n",
    "    xy8_10 = pd.concat([xy8_10,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy8_10 = pd.concat([xy8_10,ytrain8],axis=1) #adding the binary y labels for class 8\n",
    "xy8_10 = xy8_10.rename(columns={xy8_10.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy9_10 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[9]: #going through each feature in the top features list for class 9 but stopping at 10 features\n",
    "    if y >= 10: #stopping at 10 features\n",
    "        break\n",
    "    xy9_10 = pd.concat([xy9_10,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy9_10 = pd.concat([xy9_10,ytrain9],axis=1) #adding the binary y labels for class 9\n",
    "xy9_10 = xy9_10.rename(columns={xy9_10.columns[-1]:'label'}) #renaming the last column to hav the name label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "xy0_5 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[0]: #going through each feature in the top features list for class 0 but stopping at 5 features\n",
    "    if y >= 5: #stopping at 5 features\n",
    "        break\n",
    "    xy0_5 = pd.concat([xy0_5,xtrain.iloc[:,int(i)]],axis=1)  #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy0_5 = pd.concat([xy0_5,ytrain0],axis=1) #adding the binary y labels for class 0\n",
    "xy0_5 = xy0_5.rename(columns={xy0_5.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "    \n",
    "xy1_5 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[1]: #going through each feature in the top features list for class 1 but stopping at 5 features\n",
    "    if y >= 5: #stopping at 5 features\n",
    "        break\n",
    "    xy1_5 = pd.concat([xy1_5,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy1_5 = pd.concat([xy1_5,ytrain1],axis=1) #adding the binary y labels for class 1\n",
    "xy1_5 = xy1_5.rename(columns={xy1_5.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy2_5 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[2]: #going through each feature in the top features list for class 2 but stopping at 5 features\n",
    "    if y >= 5: #stopping at 5 features\n",
    "        break\n",
    "    xy2_5 = pd.concat([xy2_5,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy2_5 = pd.concat([xy2_5,ytrain2],axis=1) #adding the binary y labels for class2\n",
    "xy2_5 = xy2_5.rename(columns={xy2_5.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy3_5 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[3]: #going through each feature in the top features list for class 3 but stopping at 5 features\n",
    "    if y >= 5: #stopping at 5 features\n",
    "        break\n",
    "    xy3_5 = pd.concat([xy3_5,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy3_5 = pd.concat([xy3_5,ytrain3],axis=1) #adding the binary y labels for class 3\n",
    "xy3_5 = xy3_5.rename(columns={xy3_5.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy4_5 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[4]: #going through each feature in the top features list for class 4 but stopping at 5 features\n",
    "    if y >= 5: #stopping at 5 features\n",
    "        break\n",
    "    xy4_5 = pd.concat([xy4_5,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy4_5 = pd.concat([xy4_5,ytrain4],axis=1)  #adding the binary y labels for class 4 \n",
    "xy4_5 = xy4_5.rename(columns={xy4_5.columns[-1]:'label'})#renaming the last column to hav the name label\n",
    "\n",
    "xy5_5 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[5]: #going through each feature in the top features list for class 5 but stopping at 5 features\n",
    "    if y >= 5: #stopping at 5 features\n",
    "        break\n",
    "    xy5_5 = pd.concat([xy5_5,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy5_5 = pd.concat([xy5_5,ytrain5],axis=1) #adding the binary y labels for class 5\n",
    "xy5_5 = xy5_5.rename(columns={xy5_5.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy6_5 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[6]: #going through each feature in the top features list for class 6 but stopping at 5 features\n",
    "    if y >= 5: #stopping at 5 features\n",
    "        break\n",
    "    xy6_5 = pd.concat([xy6_5,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy6_5 = pd.concat([xy6_5,ytrain6],axis=1) #adding the binary y labels for class 6\n",
    "xy6_5 = xy6_5.rename(columns={xy6_5.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy7_5 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[7]: #going through each feature in the top features list for class 7 but stopping at 5 features\n",
    "    if y >= 5: #stopping at 5 features\n",
    "        break\n",
    "    xy7_5 = pd.concat([xy7_5,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy7_5 = pd.concat([xy7_5,ytrain7],axis=1) #adding the binary y labels for class 7\n",
    "xy7_5 = xy7_5.rename(columns={xy7_5.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy8_5 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[8]: #going through each feature in the top features list for class 8 but stopping at 5 features\n",
    "    if y >= 5: #stopping at 1 features\n",
    "        break\n",
    "    xy8_5 = pd.concat([xy8_5,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy8_5 = pd.concat([xy8_5,ytrain8],axis=1) #adding the binary y labels for class 8\n",
    "xy8_5 = xy8_5.rename(columns={xy8_5.columns[-1]:'label'}) #renaming the last column to hav the name label\n",
    "\n",
    "xy9_5 = None #initializing a variable to store\n",
    "y=0\n",
    "for i in selected_features_per_class[9]: #going through each feature in the top features list for class 9 but stopping at 5 features\n",
    "    if y >= 5: #stopping at 5 features\n",
    "        break\n",
    "    xy9_5 = pd.concat([xy9_5,xtrain.iloc[:,int(i)]],axis=1) #adding each column into the variable to store the top features\n",
    "    y+=1\n",
    "xy9_5 = pd.concat([xy9_5,ytrain9],axis=1) #adding the binary y labels for class 9\n",
    "xy9_5 = xy9_5.rename(columns={xy9_5.columns[-1]:'label'}) #renaming the last column to hav the name label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have binary datasets (Onevrsall/) we decided to try out binary classification for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current Data frame is:  0\n",
      "Accuracy: 0.977640178878569\n",
      "F1 Score: 0.9886936858584103\n",
      "Precision: 0.9781401404791514\n",
      "Recall/Sensitivity/True Positive Rate: 0.977640178878569\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [  65 2842]]\n",
      "\n",
      "\n",
      "The current Data frame is:  1\n",
      "Accuracy: 0.7640178878568972\n",
      "F1 Score: 0.8662246489859594\n",
      "Precision: 0.8197054451084171\n",
      "Recall/Sensitivity/True Positive Rate: 0.7640178878568972\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 686 2221]]\n",
      "\n",
      "\n",
      "The current Data frame is:  2\n",
      "Accuracy: 0.7715858273133815\n",
      "F1 Score: 0.8710679611650485\n",
      "Precision: 0.8237588615974939\n",
      "Recall/Sensitivity/True Positive Rate: 0.7715858273133815\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 664 2243]]\n",
      "\n",
      "\n",
      "The current Data frame is:  3\n",
      "Accuracy: 0.8555211558307534\n",
      "F1 Score: 0.9221357063403782\n",
      "Precision: 0.8763952922432349\n",
      "Recall/Sensitivity/True Positive Rate: 0.8555211558307534\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 420 2487]]\n",
      "\n",
      "\n",
      "The current Data frame is:  4\n",
      "Accuracy: 0.7994496044031648\n",
      "F1 Score: 0.8885490346014147\n",
      "Precision: 0.839670065577212\n",
      "Recall/Sensitivity/True Positive Rate: 0.7994496044031648\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 583 2324]]\n",
      "\n",
      "\n",
      "The current Data frame is:  5\n",
      "Accuracy: 0.977640178878569\n",
      "F1 Score: 0.9886936858584103\n",
      "Precision: 0.9781401404791514\n",
      "Recall/Sensitivity/True Positive Rate: 0.977640178878569\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [  65 2842]]\n",
      "\n",
      "\n",
      "The current Data frame is:  6\n",
      "Accuracy: 0.9642242862057103\n",
      "F1 Score: 0.9817863397548159\n",
      "Precision: 0.9655041879032012\n",
      "Recall/Sensitivity/True Positive Rate: 0.9642242862057103\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 104 2803]]\n",
      "\n",
      "\n",
      "The current Data frame is:  7\n",
      "Accuracy: 0.977640178878569\n",
      "F1 Score: 0.9886936858584103\n",
      "Precision: 0.9781401404791514\n",
      "Recall/Sensitivity/True Positive Rate: 0.977640178878569\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [  65 2842]]\n",
      "\n",
      "\n",
      "The current Data frame is:  8\n",
      "Accuracy: 0.9387684898520812\n",
      "F1 Score: 0.9684173172462739\n",
      "Precision: 0.942517787687076\n",
      "Recall/Sensitivity/True Positive Rate: 0.9387684898520812\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 178 2729]]\n",
      "\n",
      "\n",
      "The current Data frame is:  9\n",
      "Accuracy: 0.9735122119023047\n",
      "F1 Score: 0.9865783510545582\n",
      "Precision: 0.9742138148206131\n",
      "Recall/Sensitivity/True Positive Rate: 0.9735122119023047\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [  77 2830]]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# loop to build a model for each class and generate evaluation metrics for 5 top features \n",
    "for i in range(10):\n",
    "    strings=globals()[\"xy{}_5\".format(i)] #creating a string to keep the name of the dataframe\n",
    "    print(\"The current Data frame is: \",i) #statement to check which dataframe it is\n",
    "    y_train=strings['label'] # getting the labels \n",
    "    processed_df=strings.drop(columns=['label'])#dropping the labels from the processed data frame\n",
    "    naive_bayes_search(processed_df,y_train) # passing the inputs to build the models\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current Data frame is:  0\n",
      "Accuracy: 0.977640178878569\n",
      "F1 Score: 0.9886936858584103\n",
      "Precision: 0.9781401404791514\n",
      "Recall/Sensitivity/True Positive Rate: 0.977640178878569\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [  65 2842]]\n",
      "\n",
      "\n",
      "The current Data frame is:  1\n",
      "Accuracy: 0.7619539043687651\n",
      "F1 Score: 0.8599517440367839\n",
      "Precision: 0.6577912907654885\n",
      "Recall/Sensitivity/True Positive Rate: 0.7619539043687651\n",
      "Confusion Matrix:\n",
      " [[   5   11]\n",
      " [ 681 2210]]\n",
      "\n",
      "\n",
      "The current Data frame is:  2\n",
      "Accuracy: 0.7715858273133815\n",
      "F1 Score: 0.8710679611650485\n",
      "Precision: 0.8237588615974939\n",
      "Recall/Sensitivity/True Positive Rate: 0.7715858273133815\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 664 2243]]\n",
      "\n",
      "\n",
      "The current Data frame is:  3\n",
      "Accuracy: 0.8555211558307534\n",
      "F1 Score: 0.9221357063403782\n",
      "Precision: 0.8763952922432349\n",
      "Recall/Sensitivity/True Positive Rate: 0.8555211558307534\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 420 2487]]\n",
      "\n",
      "\n",
      "The current Data frame is:  4\n",
      "Accuracy: 0.7997936016511867\n",
      "F1 Score: 0.8871228673554946\n",
      "Precision: 0.7600001137964707\n",
      "Recall/Sensitivity/True Positive Rate: 0.7997936016511867\n",
      "Confusion Matrix:\n",
      " [[   3    2]\n",
      " [ 580 2322]]\n",
      "\n",
      "\n",
      "The current Data frame is:  5\n",
      "Accuracy: 0.977640178878569\n",
      "F1 Score: 0.9886936858584103\n",
      "Precision: 0.9781401404791514\n",
      "Recall/Sensitivity/True Positive Rate: 0.977640178878569\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [  65 2842]]\n",
      "\n",
      "\n",
      "The current Data frame is:  6\n",
      "Accuracy: 0.9642242862057103\n",
      "F1 Score: 0.9817863397548159\n",
      "Precision: 0.9655041879032012\n",
      "Recall/Sensitivity/True Positive Rate: 0.9642242862057103\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 104 2803]]\n",
      "\n",
      "\n",
      "The current Data frame is:  7\n",
      "Accuracy: 0.977640178878569\n",
      "F1 Score: 0.9886936858584103\n",
      "Precision: 0.9781401404791514\n",
      "Recall/Sensitivity/True Positive Rate: 0.977640178878569\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [  65 2842]]\n",
      "\n",
      "\n",
      "The current Data frame is:  8\n",
      "Accuracy: 0.9387684898520812\n",
      "F1 Score: 0.9684173172462739\n",
      "Precision: 0.942517787687076\n",
      "Recall/Sensitivity/True Positive Rate: 0.9387684898520812\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 178 2729]]\n",
      "\n",
      "\n",
      "The current Data frame is:  9\n",
      "Accuracy: 0.95734434124527\n",
      "F1 Score: 0.9584828866365734\n",
      "Precision: 0.9550950838770101\n",
      "Recall/Sensitivity/True Positive Rate: 0.95734434124527\n",
      "Confusion Matrix:\n",
      " [[  11   58]\n",
      " [  66 2772]]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# loop to build a model for each class and generate evaluation metrics for 10 top features\n",
    "for i in range(10):\n",
    "    strings=globals()[\"xy{}_10\".format(i)] #creating a string to keep the name of the dataframe\n",
    "    print(\"The current Data frame is: \",i) #statement to check which dataframe it is\n",
    "    y_train=strings['label'] # getting the labels \n",
    "    processed_df=strings.drop(columns=['label']) #dropping the labels from the processed data frame\n",
    "    naive_bayes_search(processed_df,y_train) # passing the inputs to build the models\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current Data frame is:  0\n",
      "Accuracy: 0.977640178878569\n",
      "F1 Score: 0.9886936858584103\n",
      "Precision: 0.9781401404791514\n",
      "Recall/Sensitivity/True Positive Rate: 0.977640178878569\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [  65 2842]]\n",
      "\n",
      "\n",
      "The current Data frame is:  1\n",
      "Accuracy: 0.7389060887512899\n",
      "F1 Score: 0.7640212232922994\n",
      "Precision: 0.7027997924669046\n",
      "Recall/Sensitivity/True Positive Rate: 0.7389060887512899\n",
      "Confusion Matrix:\n",
      " [[ 169  242]\n",
      " [ 517 1979]]\n",
      "\n",
      "\n",
      "The current Data frame is:  2\n",
      "Accuracy: 0.7715858273133815\n",
      "F1 Score: 0.8710679611650485\n",
      "Precision: 0.8237588615974939\n",
      "Recall/Sensitivity/True Positive Rate: 0.7715858273133815\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 664 2243]]\n",
      "\n",
      "\n",
      "The current Data frame is:  3\n",
      "Accuracy: 0.8531131750945993\n",
      "F1 Score: 0.893655098457527\n",
      "Precision: 0.8090299397771646\n",
      "Recall/Sensitivity/True Positive Rate: 0.8531131750945993\n",
      "Confusion Matrix:\n",
      " [[  47   54]\n",
      " [ 373 2433]]\n",
      "\n",
      "\n",
      "The current Data frame is:  4\n",
      "Accuracy: 0.8211214310285517\n",
      "F1 Score: 0.8481711220118504\n",
      "Precision: 0.7968709743426332\n",
      "Recall/Sensitivity/True Positive Rate: 0.8211214310285517\n",
      "Confusion Matrix:\n",
      " [[ 167  104]\n",
      " [ 416 2220]]\n",
      "\n",
      "\n",
      "The current Data frame is:  5\n",
      "Accuracy: 0.977640178878569\n",
      "F1 Score: 0.9886936858584103\n",
      "Precision: 0.9781401404791514\n",
      "Recall/Sensitivity/True Positive Rate: 0.977640178878569\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [  65 2842]]\n",
      "\n",
      "\n",
      "The current Data frame is:  6\n",
      "Accuracy: 0.9628482972136223\n",
      "F1 Score: 0.9790785417692177\n",
      "Precision: 0.9359521235515743\n",
      "Recall/Sensitivity/True Positive Rate: 0.9628482972136223\n",
      "Confusion Matrix:\n",
      " [[   1    5]\n",
      " [ 103 2798]]\n",
      "\n",
      "\n",
      "The current Data frame is:  7\n",
      "Accuracy: 0.9762641898864809\n",
      "F1 Score: 0.9866300933822293\n",
      "Precision: 0.9557501989863515\n",
      "Recall/Sensitivity/True Positive Rate: 0.9762641898864809\n",
      "Confusion Matrix:\n",
      " [[   0    4]\n",
      " [  65 2838]]\n",
      "\n",
      "\n",
      "The current Data frame is:  8\n",
      "Accuracy: 0.9387684898520812\n",
      "F1 Score: 0.9684173172462739\n",
      "Precision: 0.942517787687076\n",
      "Recall/Sensitivity/True Positive Rate: 0.9387684898520812\n",
      "Confusion Matrix:\n",
      " [[   0    0]\n",
      " [ 178 2729]]\n",
      "\n",
      "\n",
      "The current Data frame is:  9\n",
      "Accuracy: 0.95734434124527\n",
      "F1 Score: 0.9581863979121424\n",
      "Precision: 0.9556765057232569\n",
      "Recall/Sensitivity/True Positive Rate: 0.95734434124527\n",
      "Confusion Matrix:\n",
      " [[  12   59]\n",
      " [  65 2771]]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# loop to build a model for each class and generate evaluation metrics for 20 top features\n",
    "for i in range(10):\n",
    "    strings=globals()[\"xy{}_20\".format(i)] #creating a string to keep the name of the dataframe\n",
    "    print(\"The current Data frame is: \",i) #statement to check which dataframe it is\n",
    "    y_train=strings['label'] # getting the labels\n",
    "    processed_df=strings.drop(columns=['label']) #dropping the labels from the processed data frame\n",
    "    naive_bayes_search(processed_df,y_train) # passing the inputs to build the models\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Binary classification resulted in very high accuracies, even less effective models had a higher accuracy than models using all features. However, this may not carry over to multilabel classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the dataset again to avoid re-running previous cells\n",
    "x_train = pd.read_csv('Dataset/x_train_all.csv')\n",
    "y_train = pd.read_csv('Dataset/y_train_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train and test the model with only the 'no_of_features' top features\n",
    "def test_feature_model(no_of_features):\n",
    "    features = []\n",
    "    # traverse the dictionay with the top features of each class\n",
    "    for i in range(len(selected_features_per_class)):\n",
    "        # for each class, get the top 'no_of_features' features\n",
    "        features.append(selected_features_per_class[i][0:no_of_features])\n",
    "    features = np.concatenate(features)\n",
    "    # remove duplicates\n",
    "    features = np.unique(features)\n",
    "    features = features.tolist()\n",
    "    # convert numbers (ints) to strings\n",
    "    for i in range(len(features)):\n",
    "        features[i] = str(features[i])\n",
    "\n",
    "    # take only the top features of the dataset\n",
    "    reduced_x_train = xtrain[features]\n",
    "    # train and test the model\n",
    "    naive_bayes_search_non_binary(reduced_x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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": [
      "Cross-Validation Scores: [0.45836404 0.45836404 0.4568902  0.44985251 0.48525074]\n",
      "Mean CV Score: 0.4617443040891434\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.4757481940144479\n",
      "F1 Score: 0.46517680193532784\n",
      "Precision: 0.5257300207272383\n",
      "Recall/Sensitivity/True Positive Rate: 0.4757481940144479\n",
      "Specificity: 0.9083072369271369\n",
      "False Positive Rate: 0.09169276307286324\n",
      "Area under ROC curve: 0.8252797345785611\n",
      "Confusion Matrix:\n",
      " [[ 47 102  63  47  52   6   5   1   1   2]\n",
      " [ 10 329 199  43  93   0   0   0   0   0]\n",
      " [  2  85 220  21  59   0   1   1   3   1]\n",
      " [  2  41  59 229  67   4   7   0  14   8]\n",
      " [  3  88  95  43 283   1   2   0   1   1]\n",
      " [  0   0   2   0   0  41   4   2  19  10]\n",
      " [  0   6   9  15  15   3  51   2  14   2]\n",
      " [  0   2   0   0   1   2   5  47  17   1]\n",
      " [  1   4   3   3   0   6   4   6  91   7]\n",
      " [  0  29  14  19  13   2  25   6  18  45]]\n"
     ]
    }
   ],
   "source": [
    "# tests model with top 20 features\n",
    "test_feature_model(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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": [
      "Cross-Validation Scores: [0.43183493 0.43257185 0.43478261 0.4159292  0.44026549]\n",
      "Mean CV Score: 0.43107681572443113\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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"
     ]
    },
    {
     "data": {
      "image/png": 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QEC5cuABvb2906tQJUVFR2R5n+vTp6NmzJy5duoQPPvgAffv2xZMnT7Ks/+LFCyxYsAA///wz/vzzT0RFReHLL7/UbZ87dy42bdqEdevW4dSpU0hISMCOHTuyjWHkyJFISkrCn3/+ifDwcMydO1fXSnbv3j20bdsWKpUKR44cQUhICAYPHozU1FQAwOLFi/Htt99iwYIFuHTpEry8vNC5c2dcv349w/0aM2YMrl27Bi8vL2zatAlTp07FrFmzcO3aNcyePRtTpkzBhg0bso2VciAoX+Lj4wUAER8fX7ADHT0qhPRfTfbL0aOFETYRlWIvX74UV69eFS9fvnxdmJiYu/9DCntJTMxz/OvWrRNWVla6z0ePHhUAxI4dO3Lct2HDhmLp0qW6z9WrVxffffed7jMA8fXXX79xWxIFALFv3z69cz19+lQXCwBx48YN3T7Lly8X9vb2us/29vZi/vz5us+pqamiWrVqokuXLlnG2bhxY+Hv75/pNj8/P1GjRg2RnJyc6fbKlSuLWbNm6ZW1aNFCfP7550IIIW7fvi0AiEWLFunVqVWrlvjll1/0ymbOnCk8PDyyjLOsy/Rn5f/l9vubfZDk1qaN9Pz/3r3MfyNTKKTtbdoUf2xERMWgefPmep8TExPh7++PPXv2IDo6GqmpqXj58mWOLUhNmjTRrZubm0OtVuumnMiMmZkZatWqpfvs6Oioqx8fH4/Y2Fi0bNlSt93AwABubm7QarVZHnP06NEYMWIEDh48CE9PT/To0UMXV1hYGNq0aZNpn6GEhATcv38frVu31itv3bo1Ll68qFf25v16/vw5bt68iSFDhmDo0KG68tTUVHZILyA+YpObgYHUORKQkqH0hAAWLWIHbSLKnJkZkJiY8/JGP5hs7d2bu+MV4mje5ubmep+//PJL/P7775g9ezZOnDiBsLAwNG7cGMnJydkeJ33ioVAosk1mMqsvCvjo8NNPP8WtW7fQr18/hIeHo3nz5li6dCmA11NgFNSb9ysxMREAsHr1aoSFhemWy5cv46+//iqU85VXTJBKgu7dgW3bgCpVMm6zsAA6dCj+mIiodFAoAHPznJeOHaXW6KxGGFYoACcnqV5ujleEo3mfOnUKAwcORLdu3dC4cWM4ODggMjKyyM6XGSsrK9jb2+PcuXO6Mo1Gg9DQ0Bz3dXJywmeffYbt27fjiy++wOrVqwFILVwnTpzIdPoLtVqNypUr49SpU3rlp06dQoMGDbI8l729PSpXroxbt26hdu3aekuNGjVye7mUCSZIJUX37kBkJHD0KPDLL8Dhw0D9+tJvavPmyR0dEZV22bVWp30uIa3VderUwfbt2xEWFoaLFy+iT58+2bYEFZVRo0YhICAAO3fuREREBMaMGYOnT59mO43F2LFjceDAAdy+fRuhoaE4evQoXFxcAAA+Pj5ISEjAxx9/jPPnz+P69ev4+eefdcMejB8/HnPnzsXmzZsRERGBSZMmISwsDGPGjMk2zunTpyMgIABLlizBP//8g/DwcKxbtw4LFy4svJtRDrEPUkliYAC0b//685w5QNeuwHffAZ9/nnkLExFRbqW1Vmc2DtKiRUUyDlJ+LFy4EIMHD0arVq1ga2uLiRMnIiEhodjjmDhxImJiYtC/f38YGBhg2LBh8PLyynZ2eI1Gg5EjR+Lu3btQq9Xw9vbGd999BwCoWLEijhw5gvHjx6Ndu3YwMDCAq6urrt/R6NGjER8fjy+++AIPHjxAgwYNsGvXLtSpUyfbOD/99FOYmZlh/vz5GD9+PMzNzdG4cWOMHTu20O5FeaQQBX3gWk4lJCTAysoK8fHxUKvVRXMSIaTO2adOAUOHAj/8UDTnIaJS4dWrV7h9+zZq1KgBExOT/B+I8z7mi1arhYuLC3r27ImZM2fKHQ5lI7ufldx+f8v+iG358uVwdnaGiYkJ3N3dcfbs2Wzrx8XFYeTIkXB0dIRKpULdunX1BuEKCAhAixYtYGlpCTs7O3Tt2lVv1FYAaN++fYbh7j/77LMiub4CUSheP1778Ufg2jV54yGisiGttbp3b+lPJkeZunPnDlavXq17bDVixAjcvn0bffr0kTs0KgayJkibN2+Gr68vpk2bhtDQUDRt2hReXl5ZvpaZnJyM9957D5GRkdi2bRsiIiKwevVqVHnj0dPx48cxcuRI/PXXXzh06BBSUlLQsWNHPH/+XO9YQ4cORXR0tG6ZV1L7+bRqJT1m02qBr76SOxoionJDqVRi/fr1aNGiBVq3bo3w8HAcPnxY16eIyjZZH7G5u7ujRYsWWLZsGQCp+dLJyQmjRo3CpEmTMtRftWoV5s+fj7///jvXc888fPgQdnZ2OH78ONq2bQtAakFydXXN00zQ6RXLI7Y0164BjRpJSdLJk0C6cTKIqHwotEdsRGVcqX7ElpycjJCQEHh6er4ORqmEp6cngoODM91n165d8PDwwMiRI2Fvb49GjRph9uzZ0GQzE3V8fDwAoEKFCnrlmzZtgq2tLRo1agQ/Pz+8ePEi23iTkpKQkJCgtxQbFxdgyBBpfeLEQh3in4iIiDKSLUF69OgRNBoN7O3t9crt7e0RExOT6T63bt3Ctm3boNFosHfvXkyZMgXffvstvvnmm0zra7VajB07Fq1bt0ajRo105X369MHGjRtx9OhR+Pn54eeff8Ynn3ySbbwBAQGwsrLSLU5OTnm84gLy9wdMTaUO229MwEhERESFr1S95q/VamFnZ4cffvhBN+T7vXv3MH/+fEybNi1D/ZEjR+Ly5cs4efKkXvmwYcN0640bN4ajoyM6dOiAmzdv6g07/yY/Pz/4+vrqPickJBRvklS5MjBuHDB7NuDnB/z3v4BhqfrrIyIiKjVka0GytbWFgYEBYmNj9cpjY2Ph4OCQ6T6Ojo6oW7eu3hgULi4uiImJyTAEvY+PD3bv3o2jR4+iatWq2cbi7u4OALhx40aWdVQqFdRqtd5S7CZMACpWlPokrV9f/OcnIiIqJ2RLkIyNjeHm5oagoCBdmVarRVBQEDw8PDLdp3Xr1rhx44beiKr//PMPHB0dYWxsDAAQQsDHxwe///47jhw5kquh1sPCwgBICViJZmUFfP21tD5tGpBDvykiIiLKH1lf8/f19cXq1auxYcMGXLt2DSNGjMDz588xaNAgAED//v3h5+enqz9ixAg8efIEY8aMwT///IM9e/Zg9uzZGDlypK7OyJEjsXHjRvzyyy+wtLRETEwMYmJi8PLlSwDAzZs3MXPmTISEhCAyMhK7du1C//790bZtW72ZoEusESOA6tWB+/eBJUvkjoaIiKhsEjJbunSpqFatmjA2NhYtW7YUf/31l25bu3btxIABA/Tqnz59Wri7uwuVSiVq1qwpZs2aJVJTU3XbAWS6rFu3TgghRFRUlGjbtq2oUKGCUKlUonbt2mL8+PEiPj4+T3HHx8cLAHner1D8/LMQgBBWVkI8elT85yciWbx8+VJcvXpVvHz5Uu5QKAvr1q0TVlZWus/Tpk0TTZs2zXafAQMGiC5duhT43IV1nLIgu5+V3H5/c6qRfCrWcZDS02qBt94CLl4EfH2Bb78t3vMTkSwKaxwkjVaDE1EnEP0sGo6WjmhTrQ0MlEU7mnZMTAxmzZqFPXv24N69e7Czs4OrqyvGjh2LDh06FOm5i9P69esxduxYxMXFAQASExORlJSEihUrZrnPwIEDERcXhx07duTqHJGRkahRowYuXLgAV1dXXXl8fDyEELC2ts7/BZQRhTEOEl+DKo2USmDuXMDbG1i2DBg1CnB2ljsqIioFtl/bjjH7x+BuwuvJaquqq2Kx92J0dymayWojIyPRunVrWFtbY/78+WjcuDFSUlJw4MABjBw5En///Xem+6WkpOR6UOCSysLCAhYWFsVyLisrq2I5T3FKTk7W9TEubrLPxUb51LEj0KEDkJwMTJ0qdzREVApsv7YdH235SC85AoB7Cffw0ZaPsP3a9iI57+effw6FQoGzZ8+iR48eqFu3Lho2bAhfX1/89ddfunoKhQIrV65E586dYW5ujlmzZgEAVq5ciVq1asHY2Bj16tXDzz//rNtHCAF/f39Uq1YNKpUKlStXxujRo3XbV6xYgTp16sDExAT29vb46KOPMo1Rq9WiatWqWLlypV75hQsXoFQqcefOHQDAwoUL0bhxY5ibm8PJyQmff/45EhMTs7x2f39/vVYejUYDX19fWFtbo2LFipgwYQLSP8jZv38/3nnnHV2dDz/8EDdv3tRtT3v5qFmzZlAoFGjfvj0AqSWqa9euunpJSUkYPXo07OzsYGJignfeeQfnzp3TbT927BgUCgWCgoLQvHlzmJmZoVWrVhnmL31TcnIyfHx84OjoCBMTE1SvXh0BAQG67XFxcRg+fDjs7e1hYmKCRo0aYffu3brtv/32Gxo2bAiVSgVnZ2d8m+4JiLOzM2bOnIn+/ftDrVbrhuU5efIk2rRpA1NTUzg5OWH06NEZphArbEyQSiuFQmpFAoCNG6XHbURU7ggh8Dz5eY5LwqsEjN43GgIZe1WklY3ZNwYJrxJydbzc9s548uQJ9u/fj5EjR8Lc3DzD9vSPg/z9/dGtWzeEh4dj8ODB+P333zFmzBh88cUXuHz5MoYPH45Bgwbh6NGjAKQv3O+++w7ff/89rl+/jh07dqBx48YAgPPnz2P06NGYMWMGIiIisH//ft2UU+kplUr07t0bv/zyi175pk2b0Lp1a1SvXl1Xb8mSJbhy5Qo2bNiAI0eOYMKECbm6FwDw7bffYv369Vi7di1OnjyJJ0+e4Pfff9er8/z5c/j6+uL8+fMICgqCUqlEt27ddG9wp03qfvjwYURHR2P79swT2wkTJuC3337Dhg0bEBoaitq1a8PLywtPnjzRqzd58mR8++23OH/+PAwNDTF48OAs41+yZAl27dqFLVu2ICIiAps2bYLz/z/B0Gq1eP/993Hq1Cls3LgRV69exZw5c3RD84SEhKBnz574+OOPER4eDn9/f0yZMgXr0w1bs2DBAjRt2hQXLlzAlClTcPPmTXh7e6NHjx64dOkSNm/ejJMnT8LHxyfX9z1fCr1nVDkhayftN338sdRh29tb3jiIqMhl1vE0MSlRwB/FviQmJeYq5jNnzggAYvv27TnWBSDGjh2rV9aqVSsxdOhQvbL//e9/4oMPPhBCCPHtt9+KunXriuTk5AzH++2334RarRYJCQm5ivXChQtCoVCIO3fuCCGE0Gg0okqVKmLlypVZ7rN161ZRsWJF3eecOmk7OjqKefPm6T6npKSIqlWrZtu5+uHDhwKACA8PF0IIcfv2bQFAXLhwQa/em520ExMThZGRkdi0aZNue3JysqhcubLu/EePHhUAxOHDh3V19uzZIwBk+SLAqFGjxH/+8x+h1WozbDtw4IBQKpUiIiIi03379Okj3nvvPb2y8ePHiwYNGug+V69eXXTt2lWvzpAhQ8SwYcP0yk6cOCGUSmWWcRZGJ222IJV233wDGBkB+/cDR47IHQ0RkR6Rx/eAmjdvrvf52rVraJ1ugu7WrVvj2rVrAID//e9/ePnyJWrWrImhQ4fi999/R2pqKgDgvffeQ/Xq1VGzZk3069cPmzZt0s27uWnTJl3/IAsLC5w4cQKurq5wcXHRtSIdP34cDx48wP/+9z/duQ8fPowOHTqgSpUqsLS0RL9+/fD48eMc5/MEpE7U0dHRusGJAcDQ0DDDNV+/fh29e/dGzZo1oVardS00UVFRubmFAKQhbVJSUvTunZGREVq2bKm7d2neHOImbTzABw8eZHrcgQMHIiwsDPXq1cPo0aNx8OBB3bawsDBUrVoVdevWzXTfrP4ur1+/rjenavr7cfHiRaxfv17v78vLywtarRa3b9/O7jYUCDtpl3a1agGffQYsXSqNtH32rNSJm4jKBTMjMyT6Zd0HJs2fd/7EB798kGO9vX32om31zB9DpT9vbtSpUwcKhSLLjtjpZfYYLjtOTk6IiIjA4cOHcejQIXz++eeYP38+jh8/DktLS4SGhuLYsWM4ePAgpk6dCn9/f5w7dw6dO3fWS1SqVKkCAOjbty9++eUXTJo0Cb/88gu8vb11b6BFRkbiww8/xIgRIzBr1ixUqFABJ0+exJAhQ5CcnAwzs9zdk5x06tQJ1atXx+rVq1G5cmVotVo0atQow4wRheXNjvAKhQIA9AZkftNbb72F27dvY9++fTh8+DB69uwJT09PbNu2DaampoUST/p/A4mJiRg+fLhe37I01apVK5RzZobfpGXB118DFhZASAiwdavc0RBRMVIoFDA3Ns9x6VirI6qqq0IBRebHgQJOaid0rNUxV8dL+yLNSYUKFeDl5YXly5dn2qk27XX4rLi4uODUqVN6ZadOnUKDBg10n01NTdGpUycsWbIEx44dQ3BwMMLDwwFILTSenp6YN28eLl26hMjISBw5cgSWlpaoXbu2bkn7cu/Tpw8uX76MkJAQbNu2DX379tWdJyQkBFqtFt9++y3efvtt1K1bF/fv38/VfQCkt8wcHR1x5swZXVlqaipCQkJ0nx8/foyIiAh8/fXX6NChA1xcXPD06VO946S91fVmq0t6aZ3a37x3KSkpOHfunN69yw+1Wo1evXph9erV2Lx5M3777Tc8efIETZo0wd27d/HPP/9kul9Wf5fppxBL76233sLVq1f1/r7SlqJ8w40tSGWBnZ3UejR1KjB5MtCtGyDTa5FEVDIZKA2w2HsxPtryERRQ6HXWTkuaFnkvKpLxkJYvX47WrVujZcuWmDFjBpo0aYLU1FQcOnQIK1euzPDI503jx49Hz5490axZM3h6euKPP/7A9u3bcfjwYQDSuEMajQbu7u4wMzPDxo0bYWpqiurVq2P37t24desW2rZtCxsbG+zduxdarRb16tXL8nzOzs5o1aoVhgwZAo1Gg86dO+u21a5dGykpKVi6dCk6deqEU6dOYdWqVXm6F2PGjMGcOXNQp04d1K9fHwsXLtRLEm1sbFCxYkX88MMPcHR0RFRUFCZNmqR3DDs7O5iammL//v2oWrUqTExMMrzib25ujhEjRmD8+PGoUKECqlWrhnnz5uHFixcYMmRInmJ+08KFC+Ho6IhmzZpBqVRi69atcHBwgLW1Ndq1a4e2bduiR48eWLhwIWrXro2///4bCoUC3t7e+OKLL9CiRQvMnDkTvXr1QnBwMJYtW4YVK1Zke86JEyfi7bffho+PDz799FOYm5vj6tWrOHToEJYtW5bva8lRtj2UKEslppN2mmfPhLC3lzpsL10qdzREVAQKYyTt367+JqourKrX4dppoZP47epvhRhpRvfv3xcjR44U1atXF8bGxqJKlSqic+fO4ujRo7o6AMTvv/+eYd8VK1aImjVrCiMjI1G3bl3x008/6bb9/vvvwt3dXajVamFubi7efvttXafjEydOiHbt2gkbGxthamoqmjRpIjZv3pxjrCtWrBAARP/+/TNsW7hwoXB0dBSmpqbCy8tL/PTTTwKAePr0qRAi507aKSkpYsyYMUKtVgtra2vh6+sr+vfvr9dJ+9ChQ8LFxUWoVCrRpEkTcezYsQz3ZvXq1cLJyUkolUrRrl07IUTGkbRfvnwpRo0aJWxtbYVKpRKtW7cWZ8+e1W1P66SdFrsQUkd1AOL27duZ3psffvhBuLq6CnNzc6FWq0WHDh1EaGiobvvjx4/FoEGDRMWKFYWJiYlo1KiR2L17t277tm3bRIMGDYSRkZGoVq2amD9/vt7xq1evLr777rsM5z179qx47733hIWFhTA3NxdNmjQRs2bNyjTGtGvnSNoykXUk7aysWiXN1VapEnDzJmBpKXdERFSISvNI2kTFqTBG0mYfpLJkyBCgTh3g4UNOP0JEWTJQGqC9c3v0btwb7Z3bMzkiygQTpLLEyAiYPVtaX7AAiImRNx4iIqJSiglSWdOjB9CyJfD8OTBzptzREBERlUpMkMoahQKYN09a/+EH4Pp1eeMhIiIqhZgglUXt2gH//S+Qmiq99k9EZQrfrSHKXmH8jDBBKqsCAqTWpK1bpdG1iajUSxvxODfTWhCVZ2k/I2+OEp5XHCiyrGrcGBgwAFi/XhpE8uhRKWEiolLLwMAA1tbWunmyzMzMcj2iNVF5IITAixcv8ODBA1hbW2c7QndOOA5SPpXIcZDSi4oC6tYFkpKAPXuAD3Keh4mISjYhBGJiYnKcooOoPLO2toaDg0Omv0Dk9vubCVI+lYoECZBaj+bPl1qULlwACpBNE1HJodFokJKSIncYRCWOkZFRti1HTJCKWKlJkJ48AWrVAuLipMdtAwbIHREREZFsOJI2SSpUAL76SlqfMgV49UreeIiIiEoBJkjlgY8PULUq8O+/QFHOfExERFRGMEEqD0xNgRkzpPXZs4GnT+WNh4iIqIRjglRe9O8PNGwoJUdz58odDRERUYnGBKm8MDAA5syR1hcvlh63ERERUaaYIJUn//0v0Lat1FHb31/uaIiIiEosJkjliULx+vHa+vXAlSuyhkNERFRSMUEqb95+G+jRA9BqAT8/uaMhIiIqkZgglUezZkl9kv74AzhxQu5oiIiIShwmSOVRvXrA0KHS+oQJAAdTJyIi0sMEqbyaOhUwMwP++gv4/Xe5oyEiIipRZE+Qli9fDmdnZ5iYmMDd3R1nz57Ntn5cXBxGjhwJR0dHqFQq1K1bF3v37s3TMV+9eoWRI0eiYsWKsLCwQI8ePRAbG1vo11aiOToCX3whrfv5Aamp8sZDRERUgsiaIG3evBm+vr6YNm0aQkND0bRpU3h5eeHBgweZ1k9OTsZ7772HyMhIbNu2DREREVi9ejWqVKmSp2OOGzcOf/zxB7Zu3Yrjx4/j/v376N69e5Ffb4nz5ZeArS3wzz/Ajz/KHQ0REVHJIWTUsmVLMXLkSN1njUYjKleuLAICAjKtv3LlSlGzZk2RnJyc72PGxcUJIyMjsXXrVl2da9euCQAiODg417HHx8cLACI+Pj7X+5RIixcLAQjh4CBEYqLc0RARERWp3H5/y9aClJycjJCQEHh6eurKlEolPD09ERwcnOk+u3btgoeHB0aOHAl7e3s0atQIs2fPhkajyfUxQ0JCkJKSolenfv36qFatWpbnBYCkpCQkJCToLWXCZ58BNWoAMTHAd9/JHQ0REVGJIFuC9OjRI2g0Gtjb2+uV29vbIyYmJtN9bt26hW3btkGj0WDv3r2YMmUKvv32W3zzzTe5PmZMTAyMjY1hbW2d6/MCQEBAAKysrHSLk5NTXi+5ZDI2ll77B4B584CHD+WNh4iIqASQvZN2Xmi1WtjZ2eGHH36Am5sbevXqhcmTJ2PVqlVFfm4/Pz/Ex8frln/L0lxmvXoBb70FPHv2OlkiIiIqx2RLkGxtbWFgYJDh7bHY2Fg4ODhkuo+joyPq1q0LAwMDXZmLiwtiYmKQnJycq2M6ODggOTkZcXFxuT4vAKhUKqjVar2lzFAqX09BsmIFcOuWvPEQERHJTLYEydjYGG5ubggKCtKVabVaBAUFwcPDI9N9WrdujRs3bkCr1erK/vnnHzg6OsLY2DhXx3Rzc4ORkZFenYiICERFRWV53nLB0xPo2BFISQGmTJE7GiIiInkVU6fxTAUGBgqVSiXWr18vrl69KoYNGyasra1FTEyMEEKIfv36iUmTJunqR0VFCUtLS+Hj4yMiIiLE7t27hZ2dnfjmm29yfUwhhPjss89EtWrVxJEjR8T58+eFh4eH8PDwyFPsZeYttjeFhkpvtAFChITIHQ0REVGhy+33t6GcyVmvXr3w8OFDTJ06FTExMXB1dcX+/ft1nayjoqKgVL5u5HJycsKBAwcwbtw4NGnSBFWqVMGYMWMwceLEXB8TAL777jsolUr06NEDSUlJ8PLywooVK4rvwkuqZs2Avn2BTZuASZOAgwfljoiIiEgWCiE4EVd+JCQkwMrKCvHx8WWrP9Lt20D9+kByspQgvfee3BEREREVmtx+f5eqt9ioGNSoAXz+ubQ+cSLwRn8vIiKi8oIJEmU0eTJgaQlcuAAEBsodDRERUbFjgkQZ2dpKrUeAlCwlJckbDxERUTFjgkSZGzsWcHQEIiOBYhiIk4iIqCRhgkSZMzcH/P2l9Zkzgfh4WcMhIiIqTkyQKGuDBwP16gGPHwPz58sdDRERUbFhgkRZMzQEAgKk9YULgehoeeMhIiIqJkyQKHtduwIeHsDLl8D06XJHQ0REVCyYIFH2FApg3jxpfc0aICJC3niIiIiKARMkytk77wCdOwMaDfDVV3JHQ0REVOSYIFHuBAQASiWwfTsQHCx3NEREREWKCRLlToMGwKBB0vrEiQCn8CMiojKMCRLlnr8/YGICnDgB7N4tdzRERERFhgkS5V7VqtII2wAwaZLUJ4mIiKgMYoJEeTNxImBjA1y9CmzYIHc0RERERYIJEuWNtbU0gS0ATJ0qjY9ERERUxjBBorwbORKoVg24dw9YskTuaIiIiAodEyTKOxMTaQJbQHr9/8kTeeMhIiIqZEyQKH/69gWaNAHi44HZs+WOhoiIqFAxQaL8MTAA5syR1pcuBe7ckTceIiKiQsQEifLP2xt4910gOVnqsE1ERFRGMEGi/FMogLlzpfWffwYuXZI3HiIiokLCBIkKpkULoGdPaeoRPz+5oyEiIioUTJCo4GbNAgwNgb17gWPH5I6GiIiowJggUcHVrg0MHy6tT5jAiWyJiKjUY4JEhWPKFMDcHDh3Dti2Te5oiIiICoQJEhUOe3vgyy+l9a++AlJS5I2HiIioAJggUeH54gvAzg64cQNYvVruaIiIiPKNCRIVHkvL1+MhTZ8OJCbKGw8REVE+MUGiwjVsmNRp+8ED4Ntv5Y6GiIgoX5ggUeEyMpJe+weABQuA2Fh54yEiIsqHEpEgLV++HM7OzjAxMYG7uzvOnj2bZd3169dDoVDoLSYmJnp10m9PW+bPn6+r4+zsnGH7nLS5xahg/vc/aQDJxERg5ky5oyEiIsoz2ROkzZs3w9fXF9OmTUNoaCiaNm0KLy8vPHjwIMt91Go1oqOjdcuddBOlvrktOjoaa9euhUKhQI8ePfTqzZgxQ6/eqFGjiuQay503pyD5/nup0zYREVEpInuCtHDhQgwdOhSDBg1CgwYNsGrVKpiZmWHt2rVZ7qNQKODg4KBb7O3t9ba/uc3BwQE7d+7Eu+++i5o1a+rVs7S01Ktnbm5eJNdYLr37LvD++0BqKjB5stzREBER5YmsCVJycjJCQkLg6empK1MqlfD09ERwcHCW+yUmJqJ69epwcnJCly5dcOXKlSzrxsbGYs+ePRgyZEiGbXPmzEHFihXRrFkzzJ8/H6mpqVkeJykpCQkJCXoL5WDOHKk1acsWaQBJIiKiUkLWBOnRo0fQaDQZWoDs7e0RExOT6T716tXD2rVrsXPnTmzcuBFarRatWrXC3bt3M62/YcMGWFpaonv37nrlo0ePRmBgII4ePYrhw4dj9uzZmDBhQpaxBgQEwMrKSrc4OTnl8WrLoSZNgH79pPWJEzkFCRERlRoKIeT71rp//z6qVKmC06dPw8PDQ1c+YcIEHD9+HGfOnMnxGCkpKXBxcUHv3r0xM5MOwfXr18d7772HpUuXZnuctWvXYvjw4UhMTIRKpcqwPSkpCUlJSbrPCQkJcHJyQnx8PNRqdY5xllt37gB16wLJycC+fYC3t9wRERFROZaQkAArK6scv79lbUGytbWFgYEBYtO9Ch4bGwsHB4dcHcPIyAjNmjXDjUw6Ap84cQIRERH49NNPczyOu7s7UlNTERkZmel2lUoFtVqtt1AuVK8O+PhI6xMnAlqtvPEQERHlgqwJkrGxMdzc3BAUFKQr02q1CAoK0mtRyo5Go0F4eDgcHR0zbPvxxx/h5uaGpk2b5nicsLAwKJVK2NnZ5f4CKHe++gqwsgIuXQI2bZI7GiIiohzJ/habr68vVq9ejQ0bNuDatWsYMWIEnj9/jkGDBgEA+vfvDz8/P139GTNm4ODBg7h16xZCQ0PxySef4M6dOxlaiRISErB169ZMW4+Cg4OxaNEiXLx4Ebdu3cKmTZswbtw4fPLJJ7CxsSnaCy6PKlYEJk2S1r/+Gnj1St54iIiIcmAodwC9evXCw4cPMXXqVMTExMDV1RX79+/XddyOioqCUvk6j3v69CmGDh2KmJgY2NjYwM3NDadPn0aDBg30jhsYGAghBHr37p3hnCqVCoGBgfD390dSUhJq1KiBcePGwdfXt2gvtjwbMwZYtgyIigJWrAB4r4mIqASTtZN2aZbbTl70hh9/BD79FKhQAbh5E7C2ljsiIiIqZ0pFJ20qZwYMABo0AJ48eT3SNhERUQnEBImKj6EhEBAgrS9aBNy7J2s4REREWWGCRMWrUyfgnXekjtrTpskdDRERUaaYIFHxenMi23XrgKtX5Y2HiIgoE0yQqPi1agV06yYNGvnGEA5EREQlBRMkksfs2YBSCezaBZw8KXc0REREepggkTzq1weGDJHWOZEtERGVMEyQSD7+/oCpKXD6NLBzp9zREBER6TBBIvlUrgyMGyet+/kBqanyxkNERPT/mCCRvCZMkOZq+/tv6a02IiKiEoAJEsnLykqawBaQxkV68ULeeIiIiMAEiUqCESMAZ2cgOloaYZuIiEhmTJBIfioV8M030vrcucCjR/LGQ0RE5R4TJCoZevcGXF2BhARg1iy5oyEionKOCRKVDErl6ylIli8Hbt+WNx4iIirXmCBRydGxI+DpCaSkAFOmyB0NERGVY0yQqGSZM0f6c9Mm4MIFeWMhIqJyiwkSlSxublJ/JACYNEneWIiIqNxigkQlzzffAEZGwMGDwOHDckdDRETlEBMkKnlq1gQ++0xanzQJ0GrljYeIiModJkhUMk2ZAlhaAiEhwJYtckdDRETlDBMkKpkqVQLGj5fWJ08GkpPljYeIiMoVJkhUcvn6Avb2wK1bwPffyx0NERGVI0yQqOQyNwf8/aX1GTOkUbaJiIiKARMkKtmGDAHq1pXmZ1uwQO5oiIionGCCRCWbkREwe7a0/u23QEyMvPEQEVG5wASJSr7u3QF3d+DFC2D6dLmjISKicoAJEpV8CgUwb560vno18M8/8sZDRERlHhMkKh3atgU+/BDQaICvvpI7GiIiKuOYIFHpERAgtSb99hvw119yR0NERGVYiUiQli9fDmdnZ5iYmMDd3R1nz57Nsu769euhUCj0FhMTE706AwcOzFDH29tbr86TJ0/Qt29fqNVqWFtbY8iQIUhMTCyS66NC0qgRMGCAtD5xIiCEvPEQEVGZJXuCtHnzZvj6+mLatGkIDQ1F06ZN4eXlhQcPHmS5j1qtRnR0tG65c+dOhjre3t56dX799Ve97X379sWVK1dw6NAh7N69G3/++SeGDRtW6NdHhWzGDEClAv78E9i7V+5oiIiojJI9QVq4cCGGDh2KQYMGoUGDBli1ahXMzMywdu3aLPdRKBRwcHDQLfb29hnqqFQqvTo2Nja6bdeuXcP+/fuxZs0auLu745133sHSpUsRGBiI+/fvF8l1UiFxcgJGj5bWJ02S+iQREREVMlkTpOTkZISEhMDT01NXplQq4enpieDg4Cz3S0xMRPXq1eHk5IQuXbrgypUrGeocO3YMdnZ2qFevHkaMGIHHjx/rtgUHB8Pa2hrNmzfXlXl6ekKpVOLMmTOFdHVUZPz8AGtr4PJl4Oef5Y6GiIjKIFkTpEePHkGj0WRoAbK3t0dMFgMC1qtXD2vXrsXOnTuxceNGaLVatGrVCnfv3tXV8fb2xk8//YSgoCDMnTsXx48fx/vvvw/N/7c2xMTEwM7OTu+4hoaGqFChQpbnTUpKQkJCgt5CMrGxef0m25QpwMuX8sZDRERljuyP2PLKw8MD/fv3h6urK9q1a4ft27ejUqVK+P6NyUw//vhjdO7cGY0bN0bXrl2xe/dunDt3DseOHcv3eQMCAmBlZaVbnJycCuFqKN9GjZIet929CyxbJnc0RERUxsiaINna2sLAwACxsbF65bGxsXBwcMjVMYyMjNCsWTPcuHEjyzo1a9aEra2tro6Dg0OGTuCpqal48uRJluf18/NDfHy8bvn3339zFR8VERMTqcM2IE1F8vSpvPEQEVGZImuCZGxsDDc3NwQFBenKtFotgoKC4OHhkatjaDQahIeHw9HRMcs6d+/exePHj3V1PDw8EBcXh5CQEF2dI0eOQKvVwt3dPdNjqFQqqNVqvYVk1q+f9Op/XJw0RhIREVEhkf0Rm6+vL1avXo0NGzbg2rVrGDFiBJ4/f45BgwYBAPr37w8/Pz9d/RkzZuDgwYO4desWQkND8cknn+DOnTv49NNPAUgduMePH4+//voLkZGRCAoKQpcuXVC7dm14eXkBAFxcXODt7Y2hQ4fi7NmzOHXqFHx8fPDxxx+jcuXKxX8TKH8MDIA5c6T1JUsAtuoREVEhMZQ7gF69euHhw4eYOnUqYmJi4Orqiv379+s6bkdFRUGpfJ3HPX36FEOHDkVMTAxsbGzg5uaG06dPo0GDBgAAAwMDXLp0CRs2bEBcXBwqV66Mjh07YubMmVCpVLrjbNq0CT4+PujQoQOUSiV69OiBJUuWFO/FU8F98AHQrh1w/DgwdSqwbp3cERERURmgEILDEedHQkICrKysEB8fz8dtcjtzBnj7bWkakosXgcaN5Y6IiIhKqNx+f8v+iI2owNzdgR49pKlH3ngcS0RElF9MkKhsmD1b6pO0Z4/0uI2IiKgAmCBR2VC3LjB0qLTOiWyJiKiAmCBR2TFtGmBmJvVJ2r5d7miIiKgUY4JEZYeDA/DFF9L6V18BKSnyxkNERKUWEyQqW778EqhUCfjnH+DHH+WOhoiISikmSFS2qNXSBLYA4O8PJCbKGg4REZVOBUqQkpOTERERgdTU1MKKh6jghg8HatYEYmOB776TOxoiIiqF8pUgvXjxAkOGDIGZmRkaNmyIqKgoAMCoUaMwJ23qByK5GBsDs2ZJ6/PmAQ8fyhsPERGVOvlKkPz8/HDx4kUcO3YMJiYmunJPT09s3ry50IIjyreePQE3N+kR28yZckdDRESlTL4SpB07dmDZsmV45513oFAodOUNGzbEzZs3Cy04onxTKoG5c6X1VasA/rskIqI8yFeC9PDhQ9jZ2WUof/78uV7CRCSrDh2Ajh2l1/2//lruaIiIqBTJV4LUvHlz7NmzR/c5LSlas2YNPDw8CicyosKQ1ooUGAiEhMgbCxERlRqG+dlp9uzZeP/993H16lWkpqZi8eLFuHr1Kk6fPo3jnAeLShJXV6BvX2DTJmkKkkOHALZyEhFRDvLVgvTOO+/g4sWLSE1NRePGjXHw4EHY2dkhODgYbm5uhR0jUcF88430ZltQkJQgERER5UAhRN5m9UxJScHw4cMxZcoU1KhRo6jiKvESEhJgZWWF+Ph4qNVqucOhnIwbByxaJLUohYRInbiJiKjcye33d56/JYyMjPDbb78VKDiiYjd5sjTKdlgY8OuvckdDREQlXL5+je7atSt27NhRyKEQFSFbW6kPEiC90ZaUJG88RERUouWrk3adOnUwY8YMnDp1Cm5ubjA3N9fbPnr06EIJjqhQjR0LLFsGREYCK1dKn4mIiDKR5z5IALLte6RQKHDr1q0CBVUasA9SKbV6NTBsGFCxojR4pJWV3BEREVExyu33d74SJGKCVGqlpgKNGwN//w189dXrOduIiKhcKLJO2ukJIcAci0oNQ0MgIEBa/+474P59eeMhIqISKd8J0k8//YTGjRvD1NQUpqamaNKkCX7++efCjI2oaHTpArRqBbx8Cfj7yx0NERGVQPlKkBYuXIgRI0bggw8+wJYtW7BlyxZ4e3vjs88+w3fffVfYMRIVLoXi9RQkP/4oPW4jIiJ6Q747aU+fPh39+/fXK9+wYQP8/f1x+/btQguwpGIfpDKgSxdg1y6ga1fg99/ljoaIiIpBkfZBio6ORqtWrTKUt2rVCtHR0fk5JFHxCwiQRtTesQM4fVruaIiIqATJV4JUu3ZtbNmyJUP55s2bUadOnQIHRVQsGjQABg2S1idMAPiyARER/b98DRQ5ffp09OrVC3/++Sdat24NADh16hSCgoIyTZyISqzp04FNm4BTp4A//gA6d5Y7IiIiKgHy1YLUo0cPnDlzBra2ttixYwd27NgBW1tbnD17Ft26dSvsGImKTpUqr0fUnjRJGieJiIjKPQ4UmU/spF2GxMUBtWoBT54Aa9YAQ4bIHRERERWRIu2kvXfvXhw4cCBD+YEDB7Bv3778HJJIPtbWwOTJ0vrUqcCLF7KGQ0RE8stXgjRp0iRoNJoM5UIITJo0Kc/HW758OZydnWFiYgJ3d3ecPXs2y7rr16+HQqHQW0xMTHTbU1JSMHHiRDRu3Bjm5uaoXLky+vfvj/vpRkx2dnbOcJw5c+bkOXYqI0aOBKpXl0bWXrJE7miIiEhm+UqQrl+/jgYNGmQor1+/Pm7cuJGnY23evBm+vr6YNm0aQkND0bRpU3h5eeHBgwdZ7qNWqxEdHa1b7ty5o9v24sULhIaGYsqUKQgNDcX27dsRERGBzpl0vp0xY4becUaNGpWn2KkMUamAmTOl9TlzgMeP5Y2HiIhkla8EycrKCrdu3cpQfuPGDZibm+fpWAsXLsTQoUMxaNAgNGjQAKtWrYKZmRnWrl2b5T4KhQIODg66xd7eXi+2Q4cOoWfPnqhXrx7efvttLFu2DCEhIYiKitI7jqWlpd5x8ho7lTF9+wJNmwLx8cDs2XJHQ0REMspXgtSlSxeMHTsWN2/e1JXduHEDX3zxRaYtNVlJTk5GSEgIPD09XwekVMLT0xPBwcFZ7peYmIjq1avDyckJXbp0wZUrV7I9T3x8PBQKBaytrfXK58yZg4oVK6JZs2aYP38+UrN5gykpKQkJCQl6C5UxSqXUegQAy5YBb7RMEhFR+ZKvBGnevHkwNzdH/fr1UaNGDdSoUQP169dHxYoVsWDBglwf59GjR9BoNHotQABgb2+PmJiYTPepV68e1q5di507d2Ljxo3QarVo1aoV7t69m2n9V69eYeLEiejdu7deb/XRo0cjMDAQR48exfDhwzF79mxMmDAhy1gDAgJgZWWlW5ycnHJ9nVSKeHkB774LJCcDU6bIHQ0REckk36/5CyFw6NAhXLx4EaampmjatCnatGmTp2Pcv38fVapUwenTp+Hh4aErnzBhAo4fP44zZ87keIyUlBS4uLigd+/emJnWh+SNbT169MDdu3dx7NixbF/nW7t2LYYPH47ExESoVKoM25OSkpCUlKT7nJCQACcnJ77mXxadOwe0bClNanvhgvTYjYiIyoQiec0/ODgYu3fvBiD1A+rYsSPs7OywYMEC9OjRA8OGDdNLInJia2sLAwMDxMbG6pXHxsbCwcEhV8cwMjJCs2bNMnQOT0lJQc+ePXHnzh0cOnQoxyTG3d0dqampiIyMzHS7SqWCWq3WW6iMatEC6NlTmnokH29lEhFR6ZenBGnGjBl6/X3Cw8MxdOhQvPfee5g0aRL++OMPBAQE5Pp4xsbGcHNzQ1BQkK5Mq9UiKChIr0UpOxqNBuHh4XB0dNSVpSVH169fx+HDh1GxYsUcjxMWFgalUgk7O7tcx09l2KxZgKEhsH8/cOSI3NEQEVExy1OCFBYWhg4dOug+BwYGomXLlli9ejV8fX2xZMmSPM/F5uvri9WrV2PDhg24du0aRowYgefPn2PQ/08i2r9/f/j5+enqz5gxAwcPHsStW7cQGhqKTz75BHfu3MGnn34KQEqOPvroI5w/fx6bNm2CRqNBTEwMYmJikJycDEBqCVu0aBEuXryIW7duYdOmTRg3bhw++eQT2NjY5Cl+KqNq1waGD5fWJ04EtFp54yEiomKVp8lqnz59qteh+vjx43j//fd1n1u0aIF///03TwH06tULDx8+xNSpUxETEwNXV1fs379fd56oqCgola/zuKdPn2Lo0KGIiYmBjY0N3NzccPr0ad24TPfu3cOuXbsAAK6urnrnOnr0KNq3bw+VSoXAwED4+/sjKSkJNWrUwLhx4+Dr65un2KmMmzoV2LABOH8e2LZNeuxGRETlQp46aVevXh0///wz2rZti+TkZFhbW+OPP/7QtSqFh4ejXbt2ePLkSZEFXFJwLrZyYvp0wN9fmqvt6lXA2FjuiIiIqACKpJP2Bx98gEmTJuHEiRPw8/ODmZmZ3ptrly5dQq1atfIfNVFJ88UXgJ0dcPMmsHq13NEQEVExyVOCNHPmTBgaGqJdu3ZYvXo1Vq9eDeM3fqNeu3YtOnbsWOhBEsnGwgKYNk1anz4dePZM3niIiKhY5GscpPj4eFhYWMDAwECv/MmTJ7CwsNBLmsoqPmIrR1JSgAYNgBs3pGTJ31/uiIiIKJ+K5BFbGisrqwzJEQBUqFChXCRHVM4YGb2em23BAiDduF1ERFT25CtBIip3PvpIGkDy+XNgxgy5oyEioiLGBIkoNxQKYN48af2HH4Dr1+WNh4iIihQTJKLcat8eeP99IDUVmDxZ7miIiKgIMUEiyos5c6TWpK1bgbNn5Y6GiIiKCBMkorxo0gTo109anzhRmtCWiIjKHCZIRHk1cyagUgHHjkmT2RIRUZnDBIkor6pVA3x8pPWJEwGNRt54iIio0DFBIsqPr74CrKyA8HBg0ya5oyEiokLGBIkoPypUAPz8pPUpU4BXr+SNh4iIChUTJKL8Gj0aqFIFiIoCli+XOxoiIipETJCI8svU9PWo2rNmAU+fyhsPEREVGiZIRAXRv780ke3Tp8DcuXJHQ0REhYQJElFBGBpKg0cCwOLFwN278sZDRESFggkSUUF9+CHwzjtSR+0pU6TxkX79VfqTQwAQEZVKCiE4FHB+JCQkwMrKCvHx8VCr1XKHQ3ILDgZatcpYXrWq1LLUvXvxx0RERBnk9vubLUhEhSE6OvPye/eAjz4Ctm8v3niIiKhAmCARFZRGA4wZk/m2tAbasWP5uI2IqBRhgkRUUCdOZN85Wwjg33+lekREVCowQSIqqKwer+W3HhERyY4JElFBOTrmrt4///AxGxFRKcEEiaig2rSR3lZTKLKv5+8P1K0rTUvy/HmxhEZERPnDBImooAwMpFf5gYxJkkIhLR99JE1we+sW4OMDVKsGTJ0KxMYWf7xERJQjJkhEhaF7d2DbNmny2jdVrSqVb90qTWq7bBlQsybw5AkwcyZQvTowbBjw99/yxE1ERJniQJH5xIEiKVMajfS2WnS01DepTRuphSl9nd9/B+bPB86efV3eqRMwfrw0KndOj+uIiChfcvv9zQQpn5ggUYEJAZw6BSxYAOza9XrMpJYtgS+/BLp1k+Z6IyKiQsORtIlKOoVCai3asQO4dk161KZSSa1KPXtKHbqXLmWHbiIiGTBBIioJ6tUDvv9e6qc0dSpQsSJw+zYwejTg5AR8/TUQEyN3lERE5UaJSJCWL18OZ2dnmJiYwN3dHWff7JeRzvr166FQKPQWExMTvTpCCEydOhWOjo4wNTWFp6cnrl+/rlfnyZMn6Nu3L9RqNaytrTFkyBAkJiYWyfUR5ZqdHTB9upQorVgB1KoFPH0KzJoldej+9FOptYmIiIqU7AnS5s2b4evri2nTpiE0NBRNmzaFl5cXHjx4kOU+arUa0dHRuuXOnTt62+fNm4clS5Zg1apVOHPmDMzNzeHl5YVXr17p6vTt2xdXrlzBoUOHsHv3bvz5558YNmxYkV0nUZ6YmQEjRgAREcBvvwFvvw0kJwM//gg0aCB16D5+/HW/JSIiKlxCZi1bthQjR47UfdZoNKJy5coiICAg0/rr1q0TVlZWWR5Pq9UKBwcHMX/+fF1ZXFycUKlU4tdffxVCCHH16lUBQJw7d05XZ9++fUKhUIh79+7lKu74+HgBQMTHx+eqPlGBnTolRLduQigUQkipkRDNmwsRGChESorc0RERlQq5/f6WtQUpOTkZISEh8PT01JUplUp4enoiODg4y/0SExNRvXp1ODk5oUuXLrhy5Ypu2+3btxETE6N3TCsrK7i7u+uOGRwcDGtrazRv3lxXx9PTE0qlEmfOnMn0nElJSUhISNBbiIpVq1bA9u3SmEmffQaYmADnzwMffwzUqQMsWQLwMTERUaGQNUF69OgRNBoN7O3t9crt7e0Rk0WH1Hr16mHt2rXYuXMnNm7cCK1Wi1atWuHu/8+mnrZfdseMiYmBnZ2d3nZDQ0NUqFAhy/MGBATAyspKtzg5OeX9gokKQ926wMqVUj8lf3/A1haIjATGjJE6dH/1FSfGJSIqINn7IOWVh4cH+vfvD1dXV7Rr1w7bt29HpUqV8P333xfpef38/BAfH69b/v333yI9H1GOKlUCpk2TEqWVK6VWpLg4ICAAcHYGBg8G3mhdJSKi3JM1QbK1tYWBgQFi081HFRsbCwcHh1wdw8jICM2aNcONGzcAQLdfdsd0cHDI0Ak8NTUVT548yfK8KpUKarVabyEqEUxNpUdu165JI3S3bi116F63DmjUCPjvf4GjR9mhm4goD2RNkIyNjeHm5oagoCBdmVarRVBQEDw8PHJ1DI1Gg/DwcDg6OgIAatSoAQcHB71jJiQk4MyZM7pjenh4IC4uDiEhIbo6R44cgVarhbu7e2FcGlHxMzAAunYFTp4ETp8GevSQBqPcuxf4z3+A5s2BX38FUlPljpSIqOQrpk7jWQoMDBQqlUqsX79eXL16VQwbNkxYW1uLmJgYIYQQ/fr1E5MmTdLVnz59ujhw4IC4efOmCAkJER9//LEwMTERV65c0dWZM2eOsLa2Fjt37hSXLl0SXbp0ETVq1BAvX77U1fH29hbNmjUTZ86cESdPnhR16tQRvXv3znXcfIuNSoXr14UYOVIIU9PXb75VqybEd98JkZAgd3RERMUut9/fsidIQgixdOlSUa1aNWFsbCxatmwp/vrrL922du3aiQEDBug+jx07VlfX3t5efPDBByI0NFTveFqtVkyZMkXY29sLlUolOnToICIiIvTqPH78WPTu3VtYWFgItVotBg0aJJ49e5brmJkgUany8KEQM2YIUanS60TJykqIiROFyOXQFkREZUFuv785WW0+cbJaKpVevgQ2bgS+/VYahBIAjIyAvn2BL76Q+iwREZVhnKyWiDIyNQWGDgWuXgV27gTatAFSUoD164HGjYH33weCgtihm4jKPSZIROWRUgl07gz8+Sfw11/A//4nle3fD3h6Am5uwC+/SMkTEVE5xASJqLxzdwe2bAGuXwd8fKR54C5ckB671aoFLFwIcOR4IipnmCARkaRmTWDpUmngyW++AeztgX//lfomOTkBEyYA9+7JHSURUbFggkRE+ipWBCZPlqYvWbMGqF9fakGaP18aoXvAAODSJbmjJCIqUkyQiChzJibAkCHSdCV//AG0aycNMvnTT0DTpoCXF3DoEDt0E1GZxASJiLKnVAIffggcOwacPQv06iWVHTwIdOwINGsmDR3ADt1EVIYwQSKi3GvRAggMBG7cAEaPBszNgYsXgX79pD5MCxYA8fFyR0lEVGBMkIgo72rUABYvljp0z54NODgAd+8C48dLHbrHj5c6eBMRlVJMkIgo/ypUAPz8pA7dP/4IuLgAz55JLUk1a0otS2FhckdJRJRnTJCIqOBUKmDwYODyZWDPHuDdd6UO3Rs3Sn2U3ntP6rPEDt1EVEowQSKiwqNUAh98ABw5Apw/D3z8MWBgABw+LL311rSp9BZccrLckRIRZYsJEhEVDTc34NdfpQ7dY8dKHbrDw6VxlGrUAObNA+Li5I6SiChTTJCIqGg5OwPffSd12p4zB3B0BO7fByZOBKpVk0bqjoqSO0oiIj1MkIioeNjYSEnR7dvAunVAw4ZSh+6FC6UO3X37SnPAERGVAEyQiKh4qVTAwIHS47Z9+4AOHQCNBvjlF+Ctt6TP+/ezQzcRyYoJEhHJQ6EAvL2lDtwhIUCfPlKH7iNHgPffB5o0AdavB5KS5I6UiMohJkhEJL+33gI2bQJu3QJ8fQELC2nIgEGDpA7dc+awQzcRFSsmSERUclSrBnz7rdShe+5coHJlIDpaGozSyQkYNw64c0fuKImoHGCCREQlj7U1MGGC1KF7wwagcWMgMRFYtAioVQvo3Vt6LEdEVESYIBFRyWVsDPTvL02Ie+AA4OkpdegODASaNwf+8x9g715Aq5U7UiIqY5ggEVHJp1AAHTsChw5JQwF88glgaAgcPQr8979SC9PatezQTUSFhgkSEZUurq7Azz9LHbq//BKwtASuXgWGDJEGpQwIAJ4+lTtKIirlmCARUenk5ATMny916J4/H6hSBYiJAb76Sto2ZozUh4mIKB+YIBFR6WZlJbUk3boltSw1bQo8fw4sWQLUrg306iVNnEtElAdMkIiobDA2lvomXbgAHDwo9VnSaoEtW4AWLYD27YHdu9mhm4hyhQkSEZUtCgXw3nvSW28XL0pvwRkaAsePA506SXPArVkDvHold6REVIIxQSKisqtJE2kcpdu3gfHjAbUa+PtvYOhQqUP3rFnA48f6+2g0wLFjwK+/Sn9qNDIETkRyUwjBGSHzIyEhAVZWVoiPj4darZY7HCLKjYQEqfVo0SKpczcAmJkBgwdLo3SHhUmdu+/efb1P1arA4sVA9+5yRExEhSy3399MkPKJCRJRKZaSAmzdKr39FhYmlSkUQGb/HSoU0p/btjFJIioDcvv9XSIesS1fvhzOzs4wMTGBu7s7zp49m6v9AgMDoVAo0LVrV71yhUKR6TJ//nxdHWdn5wzb58yZU5iXRUQllZER0KcPEBoKHD4MeHllnhwBr8vHjuXjNqJyRPYEafPmzfD19cW0adMQGhqKpk2bwsvLCw8ePMh2v8jISHz55Zdo06ZNhm3R0dF6y9q1a6FQKNCjRw+9ejNmzNCrN2rUqEK9NiIq4RQKoEMHYNKk7OsJ8Xq8pfR9loioTJI9QVq4cCGGDh2KQYMGoUGDBli1ahXMzMywdu3aLPfRaDTo27cvpk+fjpo1a2bY7uDgoLfs3LkT7777boa6lpaWevXMzc0L/fqIqBSIjs5dPT8/wNYWqFcPGDAAWLVKelMuNbVo4yOiYidrgpScnIyQkBB4enrqypRKJTw9PREcHJzlfjNmzICdnR2GDBmS4zliY2OxZ8+eTOvOmTMHFStWRLNmzTB//nyk8j85ovLJ0TF39ZycpD//+Qf46SdgxAhp6hNra2ni3MmTgT/+AB4+LKpIiaiYGMp58kePHkGj0cDe3l6v3N7eHn///Xem+5w8eRI//vgjwtI6VuZgw4YNsLS0RPd0nStHjx6Nt956CxUqVMDp06fh5+eH6OhoLFy4MNPjJCUlIemNiTATEhJydX4iKgXatJHeVrt3L+uO2lWrSsMFxMcDZ84AwcHAX39J6wkJ0sS5R4++3qd2beDttwEPD2lp3Fgaj4mISoVS9dP67Nkz9OvXD6tXr4atrW2u9lm7di369u0LExMTvXJfX1/depMmTWBsbIzhw4cjICAAKpUqw3ECAgIwffr0gl0AEZVMBgbSq/wffZTxbba0t9gWLZLqVagAvP++tABSx+1r16RkKThYWq5dA27ckJaNG6V6ZmbSiN5vJk12dsV6mUSUe7K+5p+cnAwzMzNs27ZN7020AQMGIC4uDjt37tSrHxYWhmbNmsHAwEBXpv3/aQOUSiUiIiJQq1Yt3bYTJ06gbdu2CAsLQ9OmTbON5cqVK2jUqBH+/vtv1KtXL8P2zFqQnJyc+Jo/UVmyfXvGcZCcnKTkKC+v+MfFSS1LaUnTX39JLU/p1ajxOll6+21pHjkjo4JeBRFlI7ev+cvagmRsbAw3NzcEBQXpEiStVougoCD4+PhkqF+/fn2Eh4frlX399dd49uwZFi9eDKe0/gH/78cff4Sbm1uOyREgJV9KpRJ2WfxGp1KpMm1ZIqIypHt3oEsX4MQJqeO2o6P0+O2NX8pyxdpaGjrAy0v6rNUCERGvW5j++gu4ckV6ZHf7NvDLL1I9U1OgeXP9ViYHh0K9RCLKHdkfsfn6+mLAgAFo3rw5WrZsiUWLFuH58+cYNGgQAKB///6oUqUKAgICYGJigkaNGuntb21tDQAZyhMSErB161Z8++23Gc4ZHByMM2fO4N1334WlpSWCg4Mxbtw4fPLJJ7CxsSmaCyWi0sHAQJrYtjAplYCLi7QMHiyVxccDZ8/qtzI9fSolZydOvN63evXXLUweHlKncGPjwo2PiDKQPUHq1asXHj58iKlTpyImJgaurq7Yv3+/ruN2VFQUlMq8v2wXGBgIIQR69+6dYZtKpUJgYCD8/f2RlJSEGjVqYNy4cXr9koiIipSVlTSp7nvvSZ+1WuD6df1WpsuXgTt3pCUwUKqnUgFubvqP5qpUke86iMooTjWST5xqhIiK3LNnGVuZMhuo0slJv5WpWTMpkSKiDDgXWxFjgkRExU4I6c24N1uZLl2SWp/eZGwMvPWWftKUro8mUXnFBKmIMUEiohIhMRE4f/510hQcDDx6lLFelSqvk6W335Ye06Ub/oSoPGCCVMSYIBFRiSQEcOvW6xam4GBpOpT0E+0aGUmP4t58Y65atdfjPhGVUUyQihgTJCIqNZ4/B0JC9FuZMpsQ3MFBv/N38+bS0ANEZQgTpCLGBImISi0hgMhI/VamsLCMk+4aGkrDCrz5aK5GDbYyUanGBKmIMUEiojLlxQsgNFS/lSkmJmM9Ozv9zt/NmwPm5sUfL1E+MUEqYkyQiKhMEwKIitKfY+7CBSAlRb+egQHQpIn+o7latdjKRCUWE6QixgSJiMqdV69etzKlJU737mWsV6mSlCiltTK1aAFYWBR/vESZYIJUxJggEREB+Pdf/Vam0FAgOVm/jlIJNG6s/2iuTh22MpEsmCAVMSZIRESZSEqSHsW9mTT9+2/GehUq6A8x0KIFwP9LqRgwQSpiTJCIiHLp3j396VLOn5cSqTcpFECjRvpJU926UutTXmk00oS/0dGAoyPQpo3UV4oITJCKHBMkIqJ8Sk6WhhV4s5Xpzp2M9WxsAHf314/m3N2lSX6zs307MGYMcPfu67KqVYHFi4Hu3Qv1Mqh0YoJUxJggEREVouho/Vamc+ekTuFvUigAFxf9N+ZcXF63Mm3fDnz0kfQGXvr9AGDbNiZJxASpqDFBIiIqQikp0hQpb7Yy3b6dsZ6VldSy1LIlsGpV5vPQAVKSVLWqdAw+bivXmCAVMSZIRETFLDZWSpjSkqZz56QBLvNiwQKpT5Ja/XoxN+cbdeUIE6QixgSJiEhmqalAeLiULP3yC3DqVP6Oo1QClpb6SVN+FktLtk6VArn9/jYsxpiIiIgKj6Eh0KyZtDRoALz7bs77ODsDWi2QkCAtWq20xMdLS0GZm+c/uXrzs0pV8FioQJggERFR6demjdTH6N69jJ20gdd9kG7ceN3KI4T0iC4tWcrt8uxZxrK0YQueP5eW6OiCXY+xccFbtNRqwMys9D0+LCHDNDBBIiKi0s/AQHqV/6OPpITgzSQpLUFYtEj/i1ahkFp8zM2lL+KCSErKPHHK6/L8uXS85GSpw3lWnc5zS6ksnETLwqJ4kpQSNEwD+yDlE/sgERGVQJl9wTo5SclRaXjFX6PJPtHKSxKm1RZubBYWWT8SzMtibJz58YtpmAZ20i5iTJCIiEqoEvKIRlb5fXyYfomPl4ZcKEwqVeZ9sI4cyfqtxEIcpoEJUhFjgkREROVCUlLeW6+ye3xYEEePAu3bF+gQfIuNiIiICk6lAipVkpaCSE0FEhMzT54OHQLWrMn5GAXt/J4HTJCIiIio6BkaAtbW0pKenV3uEqSCdqbPg3xMk0xERERUiNKGachqSAKFQups36ZNsYXEBImIiIjklTZMA5AxScpqmIYixgSJiIiI5Ne9u/Qqf5Uq+uVVqxbaK/55wT5IREREVDJ07w506VIihmlggkREREQlh4FBgV/lLwx8xEZERESUDhMkIiIionRKRIK0fPlyODs7w8TEBO7u7jh79myu9gsMDIRCoUDXrl31ygcOHAiFQqG3eHt769V58uQJ+vbtC7VaDWtrawwZMgSJiYmFdUlERERUismeIG3evBm+vr6YNm0aQkND0bRpU3h5eeHBgwfZ7hcZGYkvv/wSbbIYE8Hb2xvR0dG65ddff9Xb3rdvX1y5cgWHDh3C7t278eeff2LYsGGFdl1ERERUesmeIC1cuBBDhw7FoEGD0KBBA6xatQpmZmZYu3ZtlvtoNBr07dsX06dPR82aNTOto1Kp4ODgoFtsbGx0265du4b9+/djzZo1cHd3xzvvvIOlS5ciMDAQ9+/fL/RrJCIiotJF1gQpOTkZISEh8PT01JUplUp4enoiODg4y/1mzJgBOzs7DBkyJMs6x44dg52dHerVq4cRI0bg8ePHum3BwcGwtrZG8+bNdWWenp5QKpU4c+ZMpsdLSkpCQkKC3kJERERlk6wJ0qNHj6DRaGBvb69Xbm9vj5iYmEz3OXnyJH788UesXr06y+N6e3vjp59+QlBQEObOnYvjx4/j/fffh0ajAQDExMTAzs5Obx9DQ0NUqFAhy/MGBATAyspKtzg5OeXlUomIiKgUKVXjID179gz9+vXD6tWrYWtrm2W9jz/+WLfeuHFjNGnSBLVq1cKxY8fQoUOHfJ3bz88Pvr6+us8JCQlMkoiIiMooWRMkW1tbGBgYIDY2Vq88NjYWDg4OGerfvHkTkZGR6NSpk65Mq9UCkFqAIiIiUKtWrQz71axZE7a2trhx4wY6dOgABweHDJ3AU1NT8eTJk0zPC0h9mlQqVZ6vkYiIiEofWR+xGRsbw83NDUFBQboyrVaLoKAgeHh4ZKhfv359hIeHIywsTLd07twZ7777LsLCwrJs0bl79y4eP34MR0dHAICHhwfi4uIQEhKiq3PkyBFotVq4u7sX8lUSERFRaSP7IzZfX18MGDAAzZs3R8uWLbFo0SI8f/4cgwYNAgD0798fVapUQUBAAExMTNCoUSO9/a2trQFAV56YmIjp06ejR48ecHBwwM2bNzFhwgTUrl0bXl5eAAAXFxd4e3tj6NChWLVqFVJSUuDj44OPP/4YlStXLr6LJyIiohJJ9gSpV69eePjwIaZOnYqYmBi4urpi//79uo7bUVFRUCpz39BlYGCAS5cuYcOGDYiLi0PlypXRsWNHzJw5U+8R2aZNm+Dj44MOHTpAqVSiR48eWLJkSaFfHxEREZU+CiGEkDuI0ighIQFWVlaIj4+HWq2WOxwiIiLKhdx+f8s+UCQRERFRScMEiYiIiCgdJkhERERE6TBBIiIiIkqHCRIRERFROkyQiIiIiNJhgkRERESUDhMkIiIionSYIBERERGlwwSJiIiIKB0mSERERETpMEEiIiIiSocJEhEREVE6hnIHQK9ptBqciDqB6GfRcLR0RJtqbWCgNJA7LCIionKHCVIJsf3adozZPwZ3E+7qyqqqq2Kx92J0d+kuY2RERETlDx+xlQDbr23HR1s+0kuOAOBewj18tOUjbL+2XabIiIiIyicmSDLTaDUYs38MBESGbWllY/ePhUarKe7QiIiIyi0+YpPZiagTGVqO3iQg8G/Cv3D7wQ1OVk5Qq9SwNLbU/1Nlqbeefpuhkn/NRLnFvoBEBDBBkl30s+hc1bsYexEXYy/m6xwmhiYZkqa0z9klVpltMzIwylcMRKUB+wISURomSDJztHTMVb0pbaegmlU1JCQl4FnSM+nP5Gd4lvwsQ1na5yRNEgDgVeorvEp9hQfPHxQ4XpWBKueEKrNELJP6xgbGBY6npGHrQ+mV1hcw/ePutL6A23puY5JUSvDnkAqDQgiRsfML5SghIQFWVlaIj4+HWq3O93E0Wg2cFzvjXsK9TPshKaBAVXVV3B5zO88/4MmaZDxL0k+iskuodOuZ1HmV+irf15gVlYEq+8eDWSRWmW1TGaoKPb68YutD6ZX2c5jV4+6C/BxS8eLPYelX1Alubr+/mSDlU2ElSMDr31wB6CVJCigAoET85pqiSZFarN5svcokycquZStt/WXqy0KPz9jAOOvHg8Y5t2a9ua4yUEGhUOTp/Fm1PpSkv0M5CSGQqk2FRmig0WoyXU/VpkKj1RRoPadzZLV+6+kt/Hr51xyvY0DTAahToQ4MlYZ6i4HSIENZdouBIm/13zyHUsF3a7LCn8PSrzgSXCZIRawwEyQg838UTmonLPJeVOZ+oFO1qblq2cqQgGVS/0XKi0KPz0hplH1ClS6xsjC2wJj9Y/D45eNMj6eAAvYW9tjbZ6/u+mVPDgo5wcgptsxaRyl/FFDkKaEqysStIIleQc6jVCgz/BLDVsDSr7gSXCZIRaywEySAz83zI1WbisTkxBxbtrJLstLqP095LvfllEtKhVL3JZj25Zmf9bQv3zyt//8xop9FY9u1bTnG2rluZ9hb2CNVm5phSUsIc7ukJZhZbhcc2iM76ZMordAiISkhx/1qWNeAWqXWJVlKhVJaxxvrRV1eSMcs9riLsFwIgQ4/d0BMYkymf2+FmeAyQSpiRZEgkbw0Wo2UbOWUZKXbdv3JdVx9eDXH41uprGBhbFF0SUBRJhLp1gsjoUk7Zl4fZxaFouwLmF9CiAxJV05JVWEkbnk6jyj6uIjedHTAUbR3bl+gY+T2+5tvsRH9PwOlAaxMrGBlYpWn/Y5FHsO7G97Nsd6Oj3cU+AebioaB0gCLvRfjoy0fQQFFpn0BF3kvKtYWXYVCAUOFYbkex0wIAa3Q5iqpOv3vaQzaOSjHYy54bwGa2DeBVmihFVoIiNfrQpSo8nwdqwTEnZ/yV6mvctWKn9uhcQpD+f3JIyokbaq1QVV11RxbH9pUayNDdJRb3V26Y1vPbZl2EC2LfQFLA4VCoWttVCH7N1Vr2dTClKNTcvw5HPv2WHZdKIFy+4tmbofGKQxMkIgKqCS2PlD+dHfpji71urAvYCnEn8PSrST+osn3RYkKQVrrQxV1Fb3yquqqfLW4lDFQGqC9c3v0btwb7Z3b8wu1FOHPYemVluACrxPaNLI95mYn7fxhJ23KDN9EJJIffw5Lr+IY8oZvsRUxJkhERESFr6SMpF0iHrEtX74czs7OMDExgbu7O86ePZur/QIDA6FQKNC1a1ddWUpKCiZOnIjGjRvD3NwclStXRv/+/XH//n29fZ2dnaFQKPSWOXPmFOZlERERUR6VlMfcsidImzdvhq+vL6ZNm4bQ0FA0bdoUXl5eePAg+4lVIyMj8eWXX6JNG/0OWy9evEBoaCimTJmC0NBQbN++HREREejcuXOGY8yYMQPR0dG6ZdSoUYV6bURERFQ6yf6Izd3dHS1atMCyZcsAAFqtFk5OThg1ahQmTZqU6T4ajQZt27bF4MGDceLECcTFxWHHjh1ZnuPcuXNo2bIl7ty5g2rVqgGQWpDGjh2LsWPH5ituPmIjIiIqfUrFI7bk5GSEhITA09NTV6ZUKuHp6Yng4OAs95sxYwbs7OwwZMiQXJ0nPj4eCoUC1tbWeuVz5sxBxYoV0axZM8yfPx+pqVmP2pqUlISEhAS9hYiIiMomWcdBevToETQaDezt7fXK7e3t8ffff2e6z8mTJ/Hjjz8iLCwsV+d49eoVJk6ciN69e+tliqNHj8Zbb72FChUq4PTp0/Dz80N0dDQWLlyY6XECAgIwffr03F0YERERlWqlaqDIZ8+eoV+/fli9ejVsbW1zrJ+SkoKePXtCCIGVK1fqbfP19dWtN2nSBMbGxhg+fDgCAgKgUmUcsdXPz09vn4SEBDg5ORXgaoiIiKikkjVBsrW1hYGBAWJjY/XKY2Nj4eDgkKH+zZs3ERkZiU6dOunKtFotAMDQ0BARERGoVasWgNfJ0Z07d3DkyJEc+wm5u7sjNTUVkZGRqFevXobtKpUq08SJiIiIyh5Z+yAZGxvDzc0NQUFBujKtVougoCB4eHhkqF+/fn2Eh4cjLCxMt3Tu3BnvvvsuwsLCdC06acnR9evXcfjwYVSsWDHHWMLCwqBUKmFnZ1d4F0hERESlkuyP2Hx9fTFgwAA0b94cLVu2xKJFi/D8+XMMGiTNyty/f39UqVIFAQEBMDExQaNGjfT2T+t4nVaekpKCjz76CKGhodi9ezc0Gg1iYmIAABUqVICxsTGCg4Nx5swZvPvuu7C0tERwcDDGjRuHTz75BDY2NsV38URERFQiyZ4g9erVCw8fPsTUqVMRExMDV1dX7N+/X9dxOyoqCkpl7hu67t27h127dgEAXF1d9bYdPXoU7du3h0qlQmBgIPz9/ZGUlIQaNWpg3Lhxen2MiIiIqPySfRyk0io+Ph7W1tb4999/OQ4SERFRKZH2klVcXBysrKyyrCd7C1Jp9ezZMwDgm2xERESl0LNnz7JNkNiClE9arRb379+HpaUlFAqF3OHIIi0LZyta8eD9Ll6838WP97x4ldf7LYTAs2fPULly5Wy78LAFKZ+USiWqVq0qdxglglqtLlc/XHLj/S5evN/Fj/e8eJXH+51dy1Ea2SerJSIiIippmCARERERpcMEifJNpVJh2rRpHGG8mPB+Fy/e7+LHe168eL+zx07aREREROmwBYmIiIgoHSZIREREROkwQSIiIiJKhwkSERERUTpMkMq5P//8E506dULlypWhUCiwY8cOve1CCEydOhWOjo4wNTWFp6cnrl+/rlfnyZMn6Nu3L9RqNaytrTFkyBAkJibq1bl06RLatGkDExMTODk5Yd68eUV9aSVOQEAAWrRoAUtLS9jZ2aFr166IiIjQq/Pq1SuMHDkSFStWhIWFBXr06IHY2Fi9OlFRUfjvf/8LMzMz2NnZYfz48UhNTdWrc+zYMbz11ltQqVSoXbs21q9fX9SXVyKtXLkSTZo00Q2E5+HhgX379um2834XrTlz5kChUGDs2LG6Mt7zwuPv7w+FQqG31K9fX7ed97qABJVre/fuFZMnTxbbt28XAMTvv/+ut33OnDnCyspK7NixQ1y8eFF07txZ1KhRQ7x8+VJXx9vbWzRt2lT89ddf4sSJE6J27dqid+/euu3x8fHC3t5e9O3bV1y+fFn8+uuvwtTUVHz//ffFdZklgpeXl1i3bp24fPmyCAsLEx988IGoVq2aSExM1NX57LPPhJOTkwgKChLnz58Xb7/9tmjVqpVue2pqqmjUqJHw9PQUFy5cEHv37hW2trbCz89PV+fWrVvCzMxM+Pr6iqtXr4qlS5cKAwMDsX///mK93pJg165dYs+ePeKff/4RERER4quvvhJGRkbi8uXLQgje76J09uxZ4ezsLJo0aSLGjBmjK+c9LzzTpk0TDRs2FNHR0brl4cOHuu281wXDBIl00idIWq1WODg4iPnz5+vK4uLihEqlEr/++qsQQoirV68KAOLcuXO6Ovv27RMKhULcu3dPCCHEihUrhI2NjUhKStLVmThxoqhXr14RX1HJ9uDBAwFAHD9+XAgh3VsjIyOxdetWXZ1r164JACI4OFgIISW0SqVSxMTE6OqsXLlSqNVq3f2dMGGCaNiwod65evXqJby8vIr6kkoFGxsbsWbNGt7vIvTs2TNRp04dcejQIdGuXTtdgsR7XrimTZsmmjZtmuk23uuC4yM2ytLt27cRExMDT09PXZmVlRXc3d0RHBwMAAgODoa1tTWaN2+uq+Pp6QmlUokzZ87o6rRt2xbGxsa6Ol5eXoiIiMDTp0+L6WpKnvj4eABAhQoVAAAhISFISUnRu9/169dHtWrV9O5348aNYW9vr6vj5eWFhIQEXLlyRVfnzWOk1Uk7Rnml0WgQGBiI58+fw8PDg/e7CI0cORL//e9/M9wX3vPCd/36dVSuXBk1a9ZE3759ERUVBYD3ujBwslrKUkxMDADo/fCkfU7bFhMTAzs7O73thoaGqFChgl6dGjVqZDhG2jYbG5siib8k02q1GDt2LFq3bo1GjRoBkO6FsbExrK2t9eqmv9+Z/X2kbcuuTkJCAl6+fAlTU9OiuKQSKzw8HB4eHnj16hUsLCzw+++/o0GDBggLC+P9LgKBgYEIDQ3FuXPnMmzjv/HC5e7ujvXr16NevXqIjo7G9OnT0aZNG1y+fJn3uhAwQSKSwciRI3H58mWcPHlS7lDKvHr16iEsLAzx8fHYtm0bBgwYgOPHj8sdVpn077//YsyYMTh06BBMTEzkDqfMe//993XrTZo0gbu7O6pXr44tW7aU6cSluPARG2XJwcEBADK89RAbG6vb5uDggAcPHuhtT01NxZMnT/TqZHaMN89Rnvj4+GD37t04evQoqlatqit3cHBAcnIy4uLi9Oqnv9853cus6qjV6nL5n6axsTFq164NNzc3BAQEoGnTpli8eDHvdxEICQnBgwcP8NZbb8HQ0BCGhoY4fvw4lixZAkNDQ9jb2/OeFyFra2vUrVsXN27c4L/vQsAEibJUo0YNODg4ICgoSFeWkJCAM2fOwMPDAwDg4eGBuLg4hISE6OocOXIEWq0W7u7uujp//vknUlJSdHUOHTqEevXqlavHa0II+Pj44Pfff8eRI0cyPHZ0c3ODkZGR3v2OiIhAVFSU3v0ODw/XS0oPHToEtVqNBg0a6Oq8eYy0OmnHKO+0Wi2SkpJ4v4tAhw4dEB4ejrCwMN3SvHlz9O3bV7fOe150EhMTcfPmTTg6OvLfd2GQu5c4yevZs2fiwoUL4sKFCwKAWLhwobhw4YK4c+eOEEJ6zd/a2lrs3LlTXLp0SXTp0iXT1/ybNWsmzpw5I06ePCnq1Kmj95p/XFycsLe3F/369ROXL18WgYGBwszMrNy95j9ixAhhZWUljh07pvda7osXL3R1PvvsM1GtWjVx5MgRcf78eeHh4SE8PDx029Ney+3YsaMICwsT+/fvF5UqVcr0tdzx48eLa9euieXLl5eb13LTmzRpkjh+/Li4ffu2uHTpkpg0aZJQKBTi4MGDQgje7+Lw5ltsQvCeF6YvvvhCHDt2TNy+fVucOnVKeHp6CltbW/HgwQMhBO91QTFBKueOHj0qAGRYBgwYIISQXvWfMmWKsLe3FyqVSnTo0EFEREToHePx48eid+/ewsLCQqjVajFo0CDx7NkzvToXL14U77zzjlCpVKJKlSpizpw5xXWJJUZm9xmAWLduna7Oy5cvxeeffy5sbGyEmZmZ6Natm4iOjtY7TmRkpHj//feFqampsLW1FV988YVISUnRq3P06FHh6uoqjI2NRc2aNfXOUZ4MHjxYVK9eXRgbG4tKlSqJDh066JIjIXi/i0P6BIn3vPD06tVLODo6CmNjY1GlShXRq1cvcePGDd123uuCUQghhDxtV0REREQlE/sgEREREaXDBImIiIgoHSZIREREROkwQSIiIiJKhwkSERERUTpMkIiIiIjSYYJERERElA4TJCIqds7Ozli0aFGu6x87dgwKhSLDvFKkL6/3lYiyxgSJiLKkUCiyXfz9/fN13HPnzmHYsGG5rt+qVStER0fDysoqX+cjIsorQ7kDIKKSKzo6Wre+efNmTJ06FREREboyCwsL3boQAhqNBoaGOf+3UqlSpTzFYWxsrJtdnIioOLAFiYiy5ODgoFusrKygUCh0n//++29YWlpi3759cHNzg0qlwsmTJ3Hz5k106dIF9vb2sLCwQIsWLXD48GG946Z/FKRQKLBmzRp069YNZmZmqFOnDnbt2qXbnv4R2/r162FtbY0DBw7AxcUFFhYW8Pb21kvoUlNTMXr0aFhbW6NixYqYOHEiBgwYgK5du2Z7zSdPnkSbNm1gamoKJycnjB49Gs+fPwcA/PTTT7CwsMD169d19T///HPUr18fL168AAD8/PPPaN68OSwtLeHg4IA+ffrozZaedi0HDhxAs2bNYGpqiv/85z948OAB9u3bBxcXF6jVavTp00d3TABo3749fHx84OPjAysrK9ja2mLKlCnIbraouLg4fPrpp6hUqRLUajX+85//4OLFi7rtFy9exLvvvgtLS0uo1Wq4ubnh/Pnz2d4fovKCCRIRFcikSZMwZ84cXLt2DU2aNEFiYiI++OADBAUF4cKFC/D29kanTp0QFRWV7XGmT5+Onj174tKlS/jggw/Qt29fPHnyJMv6L168wIIFC/Dzzz/jzz//RFRUFL788kvd9rlz52LTpk1Yt24dTp06hYSEBOzYsSPbGG7evAlvb2/06NEDly5dwubNm3Hy5En4+PgAAPr376+LLTU1FXv27MGaNWuwadMmmJmZAQBSUlIwc+ZMXLx4ETt27EBkZCQGDhyY4Vz+/v5YtmwZTp8+jX///Rc9e/bEokWL8Msvv2DPnj04ePAgli5dqrfPhg0bYGhoiLNnz2Lx4sVYuHAh1qxZk+X1/O9//9MlXiEhIXjrrbfQoUMH3X3t27cvqlatinPnziEkJASTJk2CkZFRtveIqNyQd65cIiot1q1bJ6ysrHSfjx49KgCIHTt25Lhvw4YNxdKlS3Wfq1evLr777jvdZwDi66+/1n1OTEwUAMS+ffv0zvX06VNdLAD0Zi5fvny5sLe31322t7cX8+fP131OTU0V1apVE126dMkyziFDhohhw4bplZ04cUIolUrx8uVLIYQQT548EVWrVhUjRowQ9vb2YtasWdle+7lz5wQA8ezZM71rOXz4sK5OQECAACBu3rypKxs+fLjw8vLSfW7Xrp1wcXERWq1WVzZx4kTh4uKi+/zmfT1x4oRQq9Xi1atXevHUqlVLfP/990IIISwtLcX69euzjZ+ovGILEhEVSPPmzfU+JyYm4ssvv4SLiwusra1hYWGBa9eu5diC1KRJE926ubk51Gq13qOp9MzMzFCrVi3dZ0dHR139+Ph4xMbGomXLlrrtBgYGcHNzyzaGixcvYv369bCwsNAtXl5e0Gq1uH37NgDAxsYGP/74I1auXIlatWph0qRJescICQlBp06dUK1aNVhaWqJdu3YAkOH637xee3t7mJmZoWbNmnpl6a//7bffhkKh0H328PDA9evXodFoMr2WxMREVKxYUe96bt++jZs3bwIAfH198emnn8LT0xNz5szRlRMRO2kTUQGZm5vrff7yyy9x6NAhLFiwALVr14apqSk++ugjJCcnZ3uc9I92FAoFtFptnuqLbPrj5EZiYiKGDx+O0aNHZ9hWrVo13fqff/4JAwMDREdH4/nz57C0tAQAPH/+HF5eXvDy8sKmTZtQqVIlREVFwcvLK8P1vxm/QqHI8/Xn5locHR1x7NixDNusra0BSI/5+vTpgz179mDfvn2YNm0aAgMD0a1bt3yfl6isYIJERIXq1KlTGDhwoO5LNjExEZGRkcUag5WVFezt7XHu3Dm0bdsWAKDRaBAaGgpXV9cs93vrrbdw9epV1K5dO8s6p0+fxty5c/HHH39g4sSJ8PHxwYYNGwAAf//9Nx4/fow5c+bAyckJAAq10/OZM2f0Pv/111+oU6cODAwMMr2WmJgYGBoawtnZOctj1q1bF3Xr1sW4cePQu3dvrFu3jgkSEdhJm4gKWZ06dbB9+3aEhYXh4sWL6NOnT4FaQvJr1KhRCAgIwM6dOxEREYExY8bg6dOneo+o0ps4cSJOnz4NHx8fhIWF4fr169i5c6euk/azZ8/Qr18/jB49Gu+//z42bdqEzZs3Y9u2bQCkViZjY2MsXboUt27dwq5duzBz5sxCu6aoqCj4+voiIiICv/76K5YuXYoxY8ZkWtfT0xMeHh7o2rUrDh48iMjISJw+fRqTJ0/G+fPn8fLlS/j4+ODYsWO4c+cOTp06hXPnzsHFxaXQ4iUqzdiCRESFauHChRg8eDBatWoFW1tbTJw4EQkJCcUex8SJExETE4P+/fvDwMAAw4YNg5eXV6atLWmaNGmC48ePY/LkyWjTpg2EEKhVqxZ69eoFABgzZgzMzc0xe/ZsAEDjxo0xe/ZsDB8+HB4eHqhSpQrWr1+Pr776CkuWLMFbb72FBQsWoHPnzoVyTf3798fLly/RsmVLGBgYYMyYMVkOuKlQKLB3715MnjwZgwYNwsOHD+Hg4IC2bdvC3t4eBgYGePz4Mfr374/Y2FjY2tqie/fumD59eqHESlTaKURBH9oTEZUCWq0WLi4u6NmzZ6G26hSX9u3bw9XVlVOJEBUTtiARUZl0584dHDx4EO3atUNSUhKWLVuG27dvo0+fPnKHRkSlAPsgEVGZpFQqsX79erRo0QKtW7dGeHg4Dh8+zD42RJQrfMRGRERElA5bkIiIiIjSYYJERERElA4TJCIiIqJ0mCARERERpcMEiYiIiCgdJkhERERE6TBBIiIiIkqHCRIRERFROkyQiIiIiNL5PxM6w4oQUt7NAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.4482284141726866\n",
      "F1 Score: 0.444018136755348\n",
      "Precision: 0.4846562444583077\n",
      "Recall/Sensitivity/True Positive Rate: 0.4482284141726866\n",
      "Specificity: 0.8978767784167748\n",
      "False Positive Rate: 0.10212322158322498\n",
      "Area under ROC curve: 0.7991064840298894\n",
      "Confusion Matrix:\n",
      " [[ 39  67  43  23  48   4   3   0   4   0]\n",
      " [ 15 360 195  80  89   3   0   1   4   2]\n",
      " [  1  77 205  27  55   0   0   0   6   5]\n",
      " [  4  72  77 172  83   6   5   0  12   4]\n",
      " [  4  60  96  82 273   4   1   0  12   6]\n",
      " [  0   0   5   2   1  35   2   1  14   5]\n",
      " [  0  13  15  12  14   3  53   1  14   3]\n",
      " [  0   4   7   0   7   2   9  50  19   0]\n",
      " [  1   5   4   6   1   4   5   3  77  13]\n",
      " [  1  28  17  16  12   4  26   9  16  39]]\n"
     ]
    }
   ],
   "source": [
    "# tests model with top 10 features\n",
    "test_feature_model(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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": [
      "Cross-Validation Scores: [0.36477524 0.35224761 0.35151069 0.35840708 0.36283186]\n",
      "Mean CV Score: 0.35795449357966885\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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",
      "c:\\Users\\victo\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1183: 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"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.3525971792225662\n",
      "F1 Score: 0.3557982053059759\n",
      "Precision: 0.37539567174579325\n",
      "Recall/Sensitivity/True Positive Rate: 0.3525971792225662\n",
      "Specificity: 0.8669881663735386\n",
      "False Positive Rate: 0.1330118336264613\n",
      "Area under ROC curve: 0.7274052679780639\n",
      "Confusion Matrix:\n",
      " [[ 28  68  28  14  32   5   3   0   8   0]\n",
      " [ 27 336 272 102 123   8   4   5  14   6]\n",
      " [  3  95 135  18  72   1   5   0  10  13]\n",
      " [  4  55  50 139 111   4  10   0  15   6]\n",
      " [  2  89 125 113 189  10   7   0  17   3]\n",
      " [  0   1   3   2   1  24   2   0  15   9]\n",
      " [  0  16  27  21  29   2  40   4  10   5]\n",
      " [  0   7   7   0   9   3   8  47  18   2]\n",
      " [  0   1   1   0   4   3   5   6  62   8]\n",
      " [  1  18  16  11  13   5  20   3   9  25]]\n"
     ]
    }
   ],
   "source": [
    "# tests model with top 5 features\n",
    "test_feature_model(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the ROC curve and learning curves that our model is not over-fitting, as expected from the lower accuracy.\n",
    "\n",
    "While our accuracy is low our specificity is high, so the model can effectively identify negative cases.\n",
    "\n",
    "Accuracy decreased as the number of features decreased, even at 200 features (20 from each class) we had a significantly lower accuracy than with the full dataset. We can conclude that 200 features, even if they are the top features of each class, are not enough to properly represent the dataset. Perhaps a different feature selection method and/or a larger number of features may address this."
   ]
  }
 ],
 "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",
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