GA_Feature_Selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algoritmo Genético para Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O algoritmo genético é uma metaheurística que simula o processo de seleção natural e evolução, através dos operadores de mutação, cruzamento, seleção e elitismo. Tal metaheurística é muito utilizada em problemas complexos de otimização, sendo capaz também de lidar com variáveis binárias, discretas e inteiras."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos aplicar o algoritmo genético para realizar um feature selection em um dataset da biblioteca do scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados=load_breast_cancer()\n",
    "df = pd.DataFrame(dados['data'],columns=dados['feature_names'])\n",
    "target=dados[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df,target, test_size=0.33)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criando o Algoritmo Genético"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inicializa_populacao(tamanho,features):\n",
    "    \n",
    "    population = []\n",
    "    \n",
    "    for i in range(tamanho):\n",
    "    \n",
    "        cromossomo = np.ones(features,dtype=np.bool)\n",
    "        \n",
    "        cromossomo[:int(0.3*features)]= False\n",
    "        \n",
    "        np.random.shuffle(cromossomo)\n",
    "        \n",
    "        population.append(cromossomo)\n",
    "    \n",
    "    return population\n",
    "\n",
    "\n",
    "def fitness(populacao, X_train, y_train, X_test, y_test,svm_model):\n",
    "    \n",
    "    score = []\n",
    "    \n",
    "    for cromossomo in populacao:\n",
    "        \n",
    "        svm_model.fit(X_train.iloc[:,cromossomo],y_train)\n",
    "        \n",
    "        y_pred = svm_model.predict(X_test.iloc[:,cromossomo])\n",
    "        \n",
    "        score.append(accuracy_score(y_test,y_pred))\n",
    "    \n",
    "    score, populacao = np.array(score), np.array(populacao) \n",
    "    \n",
    "    \n",
    "    return score, populacao\n",
    "\n",
    "\n",
    "def torneio(scores, populacao, rounds):\n",
    "    \n",
    "    placar = []\n",
    "    posi = []\n",
    "    \n",
    "    for i in range(rounds):\n",
    "        \n",
    "        posic = int(np.random.uniform(0,len(scores-1)))\n",
    "        \n",
    "        if i == 0:\n",
    "        \n",
    "            placar.append(scores[posic])\n",
    "            posi.append(posic)\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            if placar[-1] < scores[posic]:\n",
    "            \n",
    "                placar.append(scores[posic])\n",
    "                posi.append(posic)\n",
    "        \n",
    "        \n",
    "    return posi[-1], placar\n",
    "\n",
    "\n",
    "def crossover(pai, mae, taxa):\n",
    "    \n",
    "    filho1 = np.copy(pai)\n",
    "    filho2 = np.copy(mae)\n",
    "    \n",
    "    for i in range(len(pai)):\n",
    "    \n",
    "        r = np.random.rand()\n",
    "        \n",
    "        if r<=taxa:\n",
    "        \n",
    "            filho1[i] = mae[i]\n",
    "            filho2[i] = pai[i]\n",
    "\n",
    "        \n",
    "    return filho1, filho2\n",
    "\n",
    "\n",
    "def mutacao(pop,taxa):\n",
    "    \n",
    "    for i in range(0,np.shape(pop)[0]):\n",
    "        \n",
    "        for j in range(np.shape(pop)[1]):\n",
    "            \n",
    "            if np.random.rand() < taxa:\n",
    "                \n",
    "                pop[i][j]= not pop[i][j]\n",
    "        \n",
    "    \n",
    "    return pop\n",
    "\n",
    "\n",
    "def GA(tamanho,features,rounds,taxa_mutacao, taxa_crossover,max_gen,X_train, X_test, y_train, y_test, model):\n",
    "    \n",
    "    best_cromo = []\n",
    "    \n",
    "    best_score = []\n",
    "    \n",
    "    mean_score = []\n",
    "    \n",
    "    pop = inicializa_populacao(tamanho,features)\n",
    "\n",
    "    \n",
    "    for i in range(max_gen):\n",
    "        \n",
    "        scores, pop_fit = fitness(pop, X_train, y_train, X_test, y_test, model) \n",
    "        \n",
    "        \n",
    "        mean_score.append(np.mean(scores))\n",
    "        best_score.append(np.max(scores))\n",
    "        best_cromo.append(pop_fit[np.argmax(scores)])\n",
    "        \n",
    "        pop_ = []\n",
    "\n",
    "        for j in range(int(len(pop)/2)):\n",
    "        \n",
    "            m,placar = torneio(scores, pop_fit, rounds)\n",
    "                       \n",
    "            mae = pop_fit[m]\n",
    "     \n",
    "            p,placar = torneio(scores, pop_fit, rounds)\n",
    "            \n",
    "            pai = pop_fit[p]\n",
    "\n",
    "            \n",
    "            filho1, filho2 = crossover(pai, mae, taxa_crossover)\n",
    "            \n",
    "            pop_.append(filho1)\n",
    "            pop_.append(filho2)\n",
    "            \n",
    "        pop_ = mutacao(pop_,taxa_mutacao)\n",
    "        \n",
    "        pop = np.copy(pop_)\n",
    "        \n",
    "    return best_score, mean_score, best_cromo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_best_dt, score_mean_dt, best_cromo_dt = GA(200,30,100,0.1, 0.8,100,X_train, X_test, y_train, y_test, DecisionTreeClassifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(score_best_svm, '-o', color='red', label='Melhor Score')\n",
    "plt.title('Score a Cada Iteração - DECISION TREE CLASSIFIER')\n",
    "plt.plot(score_mean_svm, '-o', color='blue', label='Score Médio')\n",
    "plt.legend()\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Iteração')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True,  True, False, False,  True, False, False,\n",
       "       False,  True,  True, False,  True,  True,  True, False, False,\n",
       "        True, False, False, False, False,  True,  True,  True,  True,\n",
       "       False,  True, False])"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_indiv = np.argmax(score_best_dt)\n",
    "\n",
    "best_cromo_dt[best_indiv]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features selecionadas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>radius error</th>\n",
       "      <th>texture error</th>\n",
       "      <th>area error</th>\n",
       "      <th>smoothness error</th>\n",
       "      <th>compactness error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst symmetry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>59.75</td>\n",
       "      <td>268.8</td>\n",
       "      <td>0.037350</td>\n",
       "      <td>0.1186</td>\n",
       "      <td>1.1820</td>\n",
       "      <td>6.802</td>\n",
       "      <td>0.005515</td>\n",
       "      <td>0.026740</td>\n",
       "      <td>0.01951</td>\n",
       "      <td>301.0</td>\n",
       "      <td>0.10860</td>\n",
       "      <td>0.18870</td>\n",
       "      <td>0.186800</td>\n",
       "      <td>0.2376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>128.00</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.7392</td>\n",
       "      <td>1.3210</td>\n",
       "      <td>109.900</td>\n",
       "      <td>0.005539</td>\n",
       "      <td>0.026440</td>\n",
       "      <td>0.01332</td>\n",
       "      <td>2477.0</td>\n",
       "      <td>0.14080</td>\n",
       "      <td>0.40970</td>\n",
       "      <td>0.399500</td>\n",
       "      <td>0.2713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>61.05</td>\n",
       "      <td>285.7</td>\n",
       "      <td>0.023370</td>\n",
       "      <td>0.2957</td>\n",
       "      <td>1.3630</td>\n",
       "      <td>18.240</td>\n",
       "      <td>0.007440</td>\n",
       "      <td>0.011230</td>\n",
       "      <td>0.02203</td>\n",
       "      <td>364.2</td>\n",
       "      <td>0.11990</td>\n",
       "      <td>0.09546</td>\n",
       "      <td>0.093500</td>\n",
       "      <td>0.2552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>82.61</td>\n",
       "      <td>527.2</td>\n",
       "      <td>0.000692</td>\n",
       "      <td>0.4040</td>\n",
       "      <td>1.2140</td>\n",
       "      <td>32.960</td>\n",
       "      <td>0.007491</td>\n",
       "      <td>0.008593</td>\n",
       "      <td>0.02190</td>\n",
       "      <td>624.1</td>\n",
       "      <td>0.10210</td>\n",
       "      <td>0.06191</td>\n",
       "      <td>0.001845</td>\n",
       "      <td>0.2439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>79.08</td>\n",
       "      <td>455.8</td>\n",
       "      <td>0.028550</td>\n",
       "      <td>0.2005</td>\n",
       "      <td>0.8163</td>\n",
       "      <td>15.240</td>\n",
       "      <td>0.006773</td>\n",
       "      <td>0.024560</td>\n",
       "      <td>0.02662</td>\n",
       "      <td>545.2</td>\n",
       "      <td>0.14270</td>\n",
       "      <td>0.25850</td>\n",
       "      <td>0.099150</td>\n",
       "      <td>0.3469</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>88.37</td>\n",
       "      <td>596.6</td>\n",
       "      <td>0.020450</td>\n",
       "      <td>0.2541</td>\n",
       "      <td>0.6218</td>\n",
       "      <td>23.120</td>\n",
       "      <td>0.003728</td>\n",
       "      <td>0.014150</td>\n",
       "      <td>0.01647</td>\n",
       "      <td>745.3</td>\n",
       "      <td>0.08484</td>\n",
       "      <td>0.12330</td>\n",
       "      <td>0.109100</td>\n",
       "      <td>0.2542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>60.07</td>\n",
       "      <td>244.5</td>\n",
       "      <td>0.197500</td>\n",
       "      <td>0.4653</td>\n",
       "      <td>1.9110</td>\n",
       "      <td>24.200</td>\n",
       "      <td>0.009845</td>\n",
       "      <td>0.065900</td>\n",
       "      <td>0.03491</td>\n",
       "      <td>297.1</td>\n",
       "      <td>0.12210</td>\n",
       "      <td>0.37480</td>\n",
       "      <td>0.460900</td>\n",
       "      <td>0.3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>66.20</td>\n",
       "      <td>321.6</td>\n",
       "      <td>0.035810</td>\n",
       "      <td>0.3380</td>\n",
       "      <td>2.5090</td>\n",
       "      <td>19.330</td>\n",
       "      <td>0.017360</td>\n",
       "      <td>0.046710</td>\n",
       "      <td>0.03675</td>\n",
       "      <td>357.1</td>\n",
       "      <td>0.13600</td>\n",
       "      <td>0.16360</td>\n",
       "      <td>0.071620</td>\n",
       "      <td>0.2434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>85.09</td>\n",
       "      <td>512.0</td>\n",
       "      <td>0.096030</td>\n",
       "      <td>0.3106</td>\n",
       "      <td>1.5100</td>\n",
       "      <td>21.570</td>\n",
       "      <td>0.007807</td>\n",
       "      <td>0.039320</td>\n",
       "      <td>0.02860</td>\n",
       "      <td>591.2</td>\n",
       "      <td>0.13430</td>\n",
       "      <td>0.26580</td>\n",
       "      <td>0.257300</td>\n",
       "      <td>0.3113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>84.10</td>\n",
       "      <td>537.9</td>\n",
       "      <td>0.027720</td>\n",
       "      <td>0.2084</td>\n",
       "      <td>1.3500</td>\n",
       "      <td>17.580</td>\n",
       "      <td>0.005768</td>\n",
       "      <td>0.008082</td>\n",
       "      <td>0.01347</td>\n",
       "      <td>632.9</td>\n",
       "      <td>0.12890</td>\n",
       "      <td>0.10630</td>\n",
       "      <td>0.139000</td>\n",
       "      <td>0.2444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>381 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean perimeter  mean area  mean concavity  radius error  texture error  \\\n",
       "412           59.75      268.8        0.037350        0.1186         1.1820   \n",
       "219          128.00     1223.0        0.114500        0.7392         1.3210   \n",
       "548           61.05      285.7        0.023370        0.2957         1.3630   \n",
       "58            82.61      527.2        0.000692        0.4040         1.2140   \n",
       "130           79.08      455.8        0.028550        0.2005         0.8163   \n",
       "..              ...        ...             ...           ...            ...   \n",
       "387           88.37      596.6        0.020450        0.2541         0.6218   \n",
       "318           60.07      244.5        0.197500        0.4653         1.9110   \n",
       "345           66.20      321.6        0.035810        0.3380         2.5090   \n",
       "356           85.09      512.0        0.096030        0.3106         1.5100   \n",
       "457           84.10      537.9        0.027720        0.2084         1.3500   \n",
       "\n",
       "     area error  smoothness error  compactness error  symmetry error  \\\n",
       "412       6.802          0.005515           0.026740         0.01951   \n",
       "219     109.900          0.005539           0.026440         0.01332   \n",
       "548      18.240          0.007440           0.011230         0.02203   \n",
       "58       32.960          0.007491           0.008593         0.02190   \n",
       "130      15.240          0.006773           0.024560         0.02662   \n",
       "..          ...               ...                ...             ...   \n",
       "387      23.120          0.003728           0.014150         0.01647   \n",
       "318      24.200          0.009845           0.065900         0.03491   \n",
       "345      19.330          0.017360           0.046710         0.03675   \n",
       "356      21.570          0.007807           0.039320         0.02860   \n",
       "457      17.580          0.005768           0.008082         0.01347   \n",
       "\n",
       "     worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "412       301.0           0.10860            0.18870         0.186800   \n",
       "219      2477.0           0.14080            0.40970         0.399500   \n",
       "548       364.2           0.11990            0.09546         0.093500   \n",
       "58        624.1           0.10210            0.06191         0.001845   \n",
       "130       545.2           0.14270            0.25850         0.099150   \n",
       "..          ...               ...                ...              ...   \n",
       "387       745.3           0.08484            0.12330         0.109100   \n",
       "318       297.1           0.12210            0.37480         0.460900   \n",
       "345       357.1           0.13600            0.16360         0.071620   \n",
       "356       591.2           0.13430            0.26580         0.257300   \n",
       "457       632.9           0.12890            0.10630         0.139000   \n",
       "\n",
       "     worst symmetry  \n",
       "412          0.2376  \n",
       "219          0.2713  \n",
       "548          0.2552  \n",
       "58           0.2439  \n",
       "130          0.3469  \n",
       "..              ...  \n",
       "387          0.2542  \n",
       "318          0.3135  \n",
       "345          0.2434  \n",
       "356          0.3113  \n",
       "457          0.2444  \n",
       "\n",
       "[381 rows x 14 columns]"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.iloc[:,best_cromo_dt[best_indiv]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparação com RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean radius 0.05522124574073093\n",
      "mean texture 0.01773333581814114\n",
      "mean perimeter 0.05264836776317146\n",
      "mean area 0.028376127399936984\n",
      "mean smoothness 0.00554778053694842\n",
      "mean compactness 0.00936138164250398\n",
      "mean concavity 0.05758892301082759\n",
      "mean concave points 0.054599378335026906\n",
      "mean symmetry 0.0023106081087411385\n",
      "mean fractal dimension 0.003606498540504001\n",
      "radius error 0.010532595552822568\n",
      "texture error 0.004439312972774307\n",
      "perimeter error 0.016509110791014716\n",
      "area error 0.062376109653510266\n",
      "smoothness error 0.006313744392808826\n",
      "compactness error 0.0027369845896616225\n",
      "concavity error 0.00886967566689911\n",
      "concave points error 0.004120695937993622\n",
      "symmetry error 0.002564325267549126\n",
      "fractal dimension error 0.006601432959934114\n",
      "worst radius 0.13536653623777548\n",
      "worst texture 0.015235732438391223\n",
      "worst perimeter 0.13311893435745964\n",
      "worst area 0.09245894217559716\n",
      "worst smoothness 0.013064853432838038\n",
      "worst compactness 0.015528183999813141\n",
      "worst concavity 0.04198955949873387\n",
      "worst concave points 0.12151150632171631\n",
      "worst symmetry 0.011328704456854409\n",
      "worst fractal dimension 0.008339412399319871\n"
     ]
    }
   ],
   "source": [
    "for name, score in zip(dados['feature_names'],rf.feature_importances_):\n",
    "    print(name,score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0,\n",
       "  1,\n",
       "  2,\n",
       "  3,\n",
       "  4,\n",
       "  5,\n",
       "  6,\n",
       "  7,\n",
       "  8,\n",
       "  9,\n",
       "  10,\n",
       "  11,\n",
       "  12,\n",
       "  13,\n",
       "  14,\n",
       "  15,\n",
       "  16,\n",
       "  17,\n",
       "  18,\n",
       "  19,\n",
       "  20,\n",
       "  21,\n",
       "  22,\n",
       "  23,\n",
       "  24,\n",
       "  25,\n",
       "  26,\n",
       "  27,\n",
       "  28,\n",
       "  29],\n",
       " <a list of 30 Text xticklabel objects>)"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SW1fV1cfg+tNJPrScVQcAAGAr2Xd3D+jui6rq6Axhc1uSl3T3WVX1zCQ7uvvEJMcleVlV7czQcnrE+LtfrKr/lSHkdpKTuvv1E70WAAAA9mK7DahJ0t0nZeieu7jsqQvXv5Hkobv43ZdnONUMAAAA7NJmuvgCAADA5DbVggrM04HHTNNj/txnPWCS5wUAgMuiBRUAAIBZEFABAACYBQEVAACAWRBQAQAAmAUBFQAAgFkQUAEAAJgFp5n5Pji1BwAAwPJpQQUAAGAWtKACmzZF7wE9BwAAWKMFFQAAgFkQUAEAAJgFARUAAIBZEFABAACYBQEVAACAWTCLL5dgllYAAGBP0YIKAADALAioAAAAzIKACgAAwCwIqAAAAMyCgAoAAMAsCKgAAADMgoAKAADALAioAAAAzIKACgAAwCwIqAAAAMyCgAoAAMAsCKgAAADMgoAKAADALAioAAAAzIKACgAAwCwIqAAAAMyCgAoAAMAsCKgAAADMgoAKAADALAioAAAAzIKACgAAwCwIqAAAAMyCgAoAAMAsCKgAAADMgoAKAADALAioAAAAzIKACgAAwCwIqAAAAMyCgAoAAMAsCKgAAADMgoAKAADALAioAAAAzMK+e3oFAACAvcuBx7x+kuc991kPmOR52XtoQQUAAGAWBFQAAABmYVMBtaoOq6qzq2pnVR2zwf1XqapXj/e/p6oOXHf/javqq1X1W8tZbQAAALaa3QbUqtqW5IVJ7pfkkCRHVtUh6x722CRf7O6bJ3lekmevu/95Sd5w+VcXAACArWozLah3TLKzu8/p7guTvCrJ4esec3iSl47XT0hyz6qqJKmqn0tyTpKzlrPKAAAAbEWbCag3SPLphdvnjcs2fEx3X5TkS0muW1X7JfmdJM+4rAJV9biq2lFVOy644ILNrjsAAABbyGYCam2wrDf5mGckeV53f/WyCnT3sd19aHcfun379k2sEgAAAFvNZs6Del6SGy3cvmGS83fxmPOqat8kByT5QpI7JXlIVf1xkmsl+U5VfaO7/+xyrzkAAABbymYC6qlJDq6qg5J8JskRSR6+7jEnJjkqybuSPCTJW7q7k9xt7QFV9fQkXxVOAQAA2MhuA2p3X1RVRyc5Ocm2JC/p7rOq6plJdnT3iUmOS/KyqtqZoeX0iClXGgAAgK1nMy2o6e6Tkpy0btlTF65/I8lDd/McT/8+1g8AAIAriM1MkgQAAACTE1ABAACYBQEVAACAWRBQAQAAmAUBFQAAgFkQUAEAAJgFARUAAIBZEFABAACYBQEVAACAWRBQAQAAmAUBFQAAgFkQUAEAAJgFARUAAIBZEFABAACYBQEVAACAWRBQAQAAmAUBFQAAgFkQUAEAAJgFARUAAIBZEFABAACYBQEVAACAWRBQAQAAmAUBFQAAgFkQUAEAAJgFARUAAIBZEFABAACYBQEVAACAWRBQAQAAmAUBFQAAgFnYd0+vAFdcBx7z+kme99xnPWCS5wUAAKalBRUAAIBZEFABAACYBQEVAACAWRBQAQAAmAUBFQAAgFkQUAEAAJgFARUAAIBZEFABAACYBQEVAACAWRBQAQAAmAUBFQAAgFkQUAEAAJgFARUAAIBZEFABAACYBQEVAACAWRBQAQAAmAUBFQAAgFkQUAEAAJgFARUAAIBZEFABAACYBQEVAACAWRBQAQAAmIVNBdSqOqyqzq6qnVV1zAb3X6WqXj3e/56qOnBcfu+qOq2qPjD+/Jnlrj4AAABbxW4DalVtS/LCJPdLckiSI6vqkHUPe2ySL3b3zZM8L8mzx+WfS/Kz3f1jSY5K8rJlrTgAAABby2ZaUO+YZGd3n9PdFyZ5VZLD1z3m8CQvHa+fkOSeVVXd/f7uPn9cflaSq1bVVZax4gAAAGwtmwmoN0jy6YXb543LNnxMd1+U5EtJrrvuMQ9O8v7u/ub6AlX1uKraUVU7Lrjggs2uOwAAAFvIZgJqbbCsv5fHVNWtMnT7ffxGBbr72O4+tLsP3b59+yZWCQAAgK1mMwH1vCQ3Wrh9wyTn7+oxVbVvkgOSfGG8fcMkr0vyy9398cu7wgAAAGxNmwmopyY5uKoOqqorJzkiyYnrHnNihkmQkuQhSd7S3V1V10ry+iRP6e53LmulAQAA2Hp2G1DHMaVHJzk5yYeTvKa7z6qqZ1bVg8aHHZfkulW1M8lvJlk7Fc3RSW6e5Per6vTxcv2lvwoAAAD2evtu5kHdfVKSk9Yte+rC9W8keegGv/eHSf7wcq4jAAAAVwCb6eILAAAAk9tUCyp7zoHHvH6S5z33WQ+Y5HkBAAC+X1pQAQAAmAUBFQAAgFkQUAEAAJgFY1ABWApj5gGAy0sLKgAAALMgoAIAADALuvgCAMAWYKgFW4GACgDApQg7wJ6giy8AAACzIKACAAAwCwIqAAAAsyCgAgAAMAsCKgAAALNgFl8AAJjIFLMhmwmZrUwLKgAAALMgoAIAADALAioAAACzIKACAAAwCwIqAAAAs2AWXwAAgJGZl/csLagAAADMghZUgBWa4qhs4sgsALA1aEEFAABgFgRUAAAAZkFABQAAYBaMQYUlM8YQAAC+PwIqAFxBOaAGwNzo4gsAAMAsaEEFAADYA/RkuTQtqAAAAMyCgAoAAMAsCKgAAADMgoAKAADALJgkCQDgcphikpO9eYITgMtDQAWAy2CGRQBYHV18AQAAmAUtqFwhaAEBAID504IKAADALAioAAAAzIKACgAAwCwYgwqwhTn9BQCwNxFQAYDJmawOgM0QUIErPDvOAADzYAwqAAAAsyCgAgAAMAu6+ALAjJjY6vLTbR9g7yWgArNkBxMA4IpHF18AAABmQUAFAABgFgRUAAAAZmFTAbWqDquqs6tqZ1Uds8H9V6mqV4/3v6eqDly47ynj8rOr6r7LW3UAAAC2kt0G1KraluSFSe6X5JAkR1bVIese9tgkX+zumyd5XpJnj797SJIjktwqyWFJ/nx8PgAAALiEzbSg3jHJzu4+p7svTPKqJIeve8zhSV46Xj8hyT2rqsblr+rub3b3J5LsHJ8PAAAALqG6+7IfUPWQJId1938db/9Skjt199ELj/ng+JjzxtsfT3KnJE9P8u7ufvm4/Lgkb+juE9bVeFySx403b5nk7Mv/0mbjekk+p9ZeVU+tva+eWntfPbX2rlqrrqfW3ldPrb2vnlp7X71Vv7Yp3aS7t290x2bOg1obLFufanf1mM38brr72CTHbmJd9jpVtaO7D1Vr76mn1t5XT629r55ae1etVddTa++rp9beV0+tva/eql/bnrKZLr7nJbnRwu0bJjl/V4+pqn2THJDkC5v8XQAAANhUQD01ycFVdVBVXTnDpEcnrnvMiUmOGq8/JMlbeug7fGKSI8ZZfg9KcnCS9y5n1QEAANhKdtvFt7svqqqjk5ycZFuSl3T3WVX1zCQ7uvvEJMcleVlV7czQcnrE+LtnVdVrknwoyUVJ/nt3f3ui1zJXq+y6vFVrrbqeWntfPbX2vnpq7V21Vl1Prb2vnlp7Xz219r56W3JI5Hq7nSQJAAAAVmEzXXwBAABgcgIqAAAAsyCgwhZVVdv29DrAqtXgRrt/JAAwRwLqXq6qrlZVt5y4xraq+pMpa1xRVNVDq+qa4/Xfq6q/rarbTVRuZ1X9SVUdMtHz71JV7VNV+0/4/A+sqi31+TX+n718hfX2qaoPrqjWtqp60ypqjTPI/79V1FrlezbW+o1V1Fqot5LP/VVuH1t1u99F/Uk/h8caN6mqe43Xr7b2/bbkGiv9bFyoe/2quvHaZUU1p/7ufFJV7T8eyDuuqt5XVfeZqNZ1pnjeTdS9dlXdeuIaR1fVtaescUW3pXbw5qCq/nj8579SVb25qj5XVY+cqNbPJjk9yT+Ot29TVetPAXS5jTMv376qatnPvStVdeeqOrWqvlpVF1bVt6vqyxPVWtl7luT3u/srVXXXJPdN8tIkfzFRrVsn+WiSF1fVu6vqcRN/8b1i/Dvul2Hm7rOr6skTlTsiycfG9+5HJqqRJKmqW1TV/6mqf6qqt6xdll1n/D/bPp7Oa3Ld/Z0kZ6xix2t8bV+rqgOmrjV6d1XdYeoiq3zPxlqHT11nXb2VfO6vcvvY4tv9Sj+Hq+pXkpyQ5EXjohtmgoNDq/5srKoHVdXHknwiySlJzk3yhgnrrfK78zHd/eUk90myPcmjkzxrolrvqarXVtX9p/4cqaq3jX/D6yQ5I8nxVfW/Jiz5g0lOrarXVNVhU76+qvrJqnpjVX20qs6pqk9U1TlT1ZsLs/guWVWd3t23qaqfT/JzSX4jyVu7+8cnqHVakp9J8rbuvu247MzuXvqRo6p6bobz2L42yX+uLe/uv112rbHejgwh5LVJDk3yy0lu3t3/3wS1Vvmevb+7b1tV/zPJB7r7FWvLll1rXd2fSvLKJNfKsEPxB929c8k11v6Oj0hy+yS/k+S0KbbHsd7+SY7M8AXbSY5P8sru/sqS65yR5C+TnJbku6fJ6u7TlllnrPWiJLfLcA7pxf+zSb5ox6B9hwznp16s96AJar0myZ2TvHFdrSdOUOtDSW6R5JNjrRpKTfLZuLL3rKr+KMkBSV69rtb7ll1rrLeyz/0Vbx9bcrsf663sc7iqTk9yxyTvWdgH+UB3/9gEtVb5f3ZGhn2rN43f1/dIcmR3P27ZtcZ6q3zPzuzuW1fV8zPsO75uqn2QMbTdK8ljMmwnr07yf7v7oxPUWtu3+q9JbtTdT5tqf3ihZmUI+o/OsJ/6miTHdffHl1znIxn2S9fvg3x+mXXmZrfnQeV7dqXx5/0z7Cx/YcIDKxd195dW1LB5nSSfz/ChvaaTTBJQk6S7d1bVtvHo6fFV9S8TlVrle/aZ8Yv2XkmeXVVXyUQ9GWoYg/qADB+eByZ5bpK/TnK3JCdl2IFfpitV1ZUyhPw/6+5vTbltdveXq+pvklwtya8n+fkkT66q/93dL1hiqYu6e6pW7vXOHy/7JFl6V7kNPGMFNda8fryswv1WVCdZ7Xv2E+PPZy4s61zyc3mZVvm5v8rtY6tu98nGn8NTtUR8s7svXPucr6p9M2wfU1jl/9m3uvvzNXS33ae731pVz56w3irfs9Oq6p+SHJTkKTV0yf7OFIXG4RZvTPLGMeS/PMkTxgMAx3T3u5ZYbt+q+qEkD0uy9IaMjXR3V9W/Jfm3JBcluXaSE6rqjd3920ss9aXunqwFf64E1OX7+/Fox9cz/CNuT/KNiWp9sKoenmRbVR2c5IlJJglx3f3oKZ73Mnxt7M5zelX9cZJ/TbLfRLVW+Z49LMlhSZ7T3f8xfqBO1ZXnY0nemuRPuntxuzhhbFFdthdl6Ap1RpK3V9VNknxpgjqpqgdlCN43S/KyJHfs7s9W1dWTfDjJMgPq31fVE5K8Lsk31xZ29xeWWGPtOZ+RJONOQ3f3V5ddY129U6rqBzK0JiXJe7v7sxPVeun4P712YOTs7v7WRLU+WVU/nuFgTJL8c3efMVGtlb1n3X2PqZ57F/VW9rm/4u1jS273o40+hycZHpPklKr63SRXq6p7J3lCkr+fotDC/9l+3f2fu3v85fQfVXWNJP+c5K+r6rMZAshUVvmePTbJbZKc091fG7vETvJ/XlXXTfLIJL+U5N+T/FqGFvDbZOiVcdASyz0jyclJ3tHdp1bVTTPsA02iqp6Y5Kgkn0vy4iRPHg8s7DPWXWZAfWsN8wH8bS65DzJJz5m50MV3AjUMnP5yd3973GHev7v/bYI6V89wpGhtgPvJSf6wu5cerqrqFhnGSv5Ad/9oDQPQH9Tdf7jsWmO9m2T4QLtyhq4NByT582V3S12ot6r37GZJzuvub1bV3TOME/2r7v6PCWrdtbvfsW7ZT3b3O5dda3zug7r7Ewu3K0O37KV/SVTVSzN0pXn7Bvfds7vfvMRan9hgcXf3TZdVY6HWj2YI3GuTS3wuyS9391nLrjXWe1iSP0nytgzdYO+W4Yv2hAlq3T3DmOtzx1o3SnLURu/hEmo9Kcmv5OKWvp9PcuySW9bXaq3sPathLOPTkqwdYDolyTO7e6oDQTfMcLDnJzO0jL0jyZO6+7wJat09q9s+tuR2fxnrsG93Lz1gjTvjjy7uCn4AACAASURBVM2wD1IZ9kFe3BPsWFbVXZIcl+Qa3X3j8QDU47v7CRPU2i/DAet9kjwiw/7HX6+yS+WE79lPJjm9u/+zhrk2bpfk+d39yQlqfTTDZ+Px6z8zqup3untprdIb7dtMvL/zjCQv2ejvVlU/0t0fXmKtt26wuLt7qp4z89DdLku8ZBgreanLBHW2ZWgZW9XrOiXDGIL3Lyz74MQ1r5bklit4bQ9Ncs3x+u9l2Km93US1Ts/Qc+HmST6e5HlJTpqo1vs2s2zieqdNUGdbhrFBk24Xe+KSoQfEPRZu3z3Jv0xY74wk11+4vT3JGRPVOm3x/zlDi9LSt4/xuc9Mst/C7f2SnLm3v2dJ/iZDS8FNx8vTkvzthNvHGzO0ruw7Xh6V5I1bYPvYktv9+PxP3egyQZ1tSV4+1evYoN57MoT7leyDJLlJknuN16++to8wUa0nJdk/Q8g/Lsn7ktxnolpnjnV+fLz+pCSnTFTrYRsse+hEtVa2v5PhwMWk+78urYvvBBZnjrxqkntm+LD5q2UW6aGl7/bLfM7duHp3v3fdmMLJurzUMEPxczK0oB5UVbfJ0FKw9EksMsys+9q6eGbd52RoLb7TBLW+090XVdUvJPnT7n5BVb1/mQXGI80/kWHWw99cuGv/DDsVS1VVP5zkVkkOGF/XYr2rLrveuO1/raoO6IlajhaNY4P+Wy5utXpbkhf1NN309uvu7x4t7e63jUfzp7JPX7Jr4+cz3ezuV+rus9dudPdHx7/tFCoLk0mM16caEL3K9+xm3f3ghdvPqGGimqls7+7jF27/36r69YlqrXL72KrbfbIwgVCGz98HZhj2sFTj5/D2qrpyd1+47OffRc1Pr9sH+fauHnt51DA78eMy9Iq4WZIbZJgo755T1Msws+7zq+q+uXhm3eOT/NMEtS7q7q6qwzO0nB5XVUdNUCdJjskwcdCip2To3rsUq97fSYaZwKvqjKq6cXd/aooai1bdc2YuBNQl6+5fW7w9blgvm6jc+2s4rcwqZtb93Ng9tZOkqh6SYVzoVJ6eocX2bUnS3adX1YET1Vr7kntAkr/o7r+rqqdPVOtbVXVkhpb1nx2XLXtn5cpJrpHh/3txMokvJ3nIkmslyS0z7ARdKxe/piT5SoZullP4RpIPVNUqZsb8iwzv0Z+Pt39pXPZfJ6h1TlX9fi7+zHhkhlMdTOUfq+rkDDM8J8kvZphAawo7quq4XPzaHpGhdWkKx2c4xcHrxts/l6FlYgqrfM++vth1f+yu9/WJaiXD5/4jc/H2cWSGMDeFVW4fW3W7T3c/d/F2VT0nw7i/KZyb5J3jfsjUs45/uqp+IkmPY3qfmAmC9+i/Z5ydOEm6+2NVdf2JaiUXHzy7f4busGdUTTbD4Feq6ikZvsfuVsNkikvdB6mq+2V4LTeoqv+9cNf+WX7Dxqr3d9b8UJKzqmrymcCTvCTJBzPMYZIM793xSX5hl7+xBRiDOrHxSOmZ3b30czVW1fEbLO7ufswEtW6a5NgMR6q+mGEH7BE9wbiFsd57uvtOtTD9eU13Cp1/SPKZDDPr3j7DDt97e5rTzByS5FeTvKu7X1lVByX5xe5e+nnIquomU70/u6h3l17urHyXVWujI77d3UvtqTDWOmP9trDRsiXVunaGLpx3HRe9PckzuvuLy661UPMXxnqV5O3d/brd/Mr3W+cqGXb8vlsrw7jyb17mL37/9W6XS76upfZUWKizsvdsHHf3VxnGxCXDZ/FR3X3msmuN9W6c5M+S3CXDwcl/yTAGdYrxaqvePrbkdr9B/Wtn+D47eILnftpGy3uc0GjJta6X5PkZvqcrQ+viE3uCyerW73/UMDvx+6bY/xjrHZ+hlfagDF1vt2U4BczSe8lV1Q8meXiSU7v7n8f/8bsv87tz/Jy6TYbZxp+6cNdXMpzCb4rPxlXv7/z0Rsu7+5QJap3e3bfZ3bKtRgvqklXV3+fiadb3SXJILt3FYSl6tTPrdnffa+y6tk93f2UMV1NZ2QzFWeHMut39oar6nSQ3Hm9/Iks+SXZV/Wl3/3qSP6sNpqqf6AhfkuysYUbHA7Pw2TLFAZMk1+ru5y8uqGFinCl8u6pu1uO5zcaDNUvvWjYeyf7diVqBd1Xv5O6+VyY8XdRCreO6+5FJpjx5+trELWd2949mGF4xZa2VvWfj67pld/94DecATndPNdPn2mt78ISfF+trrWr72JLb/ULND+TifZBtGbqMPnPXv/H9myKIXoZbdvcjFheMPQimmATnlFrR7MSj9TPrXjcTzazb3f9Ww+nZ1g5YfC7DDPXLrHFGkjOq6q97gomeduEqVXVsLr3/MdVEQvfv7t9ZXFDDqYiWHlCz+p4zsyCgLt9zFq5flOSTPcGMh8l3j7ptFECmCAR/k2HioMXxLSdkaHGcwq9lmKH4m0lekXGG4ikKjV8In81whPtjGd63SaYnr9WMrV3rSvacy3zU8v1dhmn535SJxgYtOCrD0fRFj9pg2TI8OcM07+dkOHJ/k0yw87DqceWrHMu7yvFqqxwftMr3bHxdRyd5zZTBdKHet8dxas9bUa1VbR9bcrtf8MCF6xcl+fepQkINs4tutA8yRSh4QYYZZ3e3bBmOyRAaP5Dk8Rm6f794gjprOkNjxgMzHEzYLxPM35CsZnxtVb2mux+WYRjaRtvHFC3Rr83wOl6c6fc/kuTeSX5n3bL7bbBsGf5bkpfWMGSwknwhw/7OliagLtkUzfuX4R8Wrl81w6kUzl9mgVrxBDhjzW0Zusg9OSs44fLYTenQDGMpj88wHuPlGU6tsGxPz6XH1i61Jbq718Y3XSfDDMEr6UqWYSKtKT6cv6uG8bsPzxDuF8dVXTMTjY3r7jePrfi3zPDl8JEJ/6arHFeerHYs77lZ3Xi1VY4PWuV79saq+q0kr15Xa+ndHEfvrKo/26DeFC3T52Z128eW2+6rav/xwMVX1t21f1VNtY381sL1qyZ5cJY8xrBWP+nftiQvHVu9/8+yn38X/jzJd5L8TIaA+pUMjQJ3uKxf+j6tYnztWm+mB17mo5brou7+i6mLVNV/y9CiftOqWhxacc1M1Muvu09PspKeM3MioC5JVb2ju+9aVV/JJY8oVobusfsvu2Z3/826dXhlhtarZVr5BDirbknKEOxvm7E7YHefX1XXvOxf+b5d1N1fqkvOfzDVQPAHJfnTqnp7kldl6NY2ZXebf6iq+3f3VJONJMMXwL8muV6SxclAvpJhyvylqaqf6e63rDswkyQ3G3f4pggg18kQtBdbIDrTdUV8/XhZhfPHyz655GQWU1hl18NVvmdrvWP++7paSz8n7+gnxp+LXUQ7l3yty7LK7WMrbvevyPBdfVqG92jxS2aSbWThYOiad1bVsg/Sr3QSnD3U6n2n7r5djTP6d/cXa5gIagrf7O4L1/ZBxvG1S90H6e61CTR/IUOPj88s8/l34e+r6gkZuit/9wDyBAdmXpHkDUn+Z4aW9jVfWXatqnpkd7983YGZrL13Ex28mw0BdUm6+67jz6m/WC/LwRnHNi5Ld/9dkr+rFU6AM1plq8SF3d1rXVFq2tN6rGxsbXc/uoZJuu6XodXxz6vqjd09xeyzyXDU9Her6ptJvpUJDs6MkyB8MsldquomSQ7u7jdV1dUynDd3fevB5fHTSd6SSx6Y+e6qZMkBZDxyf2Z3T96lcqHevceWglXUusbYK2IVtX5/HGO4ilorec/GMaiP7IlOPL+Len/R3ZPMobCu1qq3jy233Xf3A8efU84NcQlVdZ2Fm/tkGPLzg8usMfZKO6Wq/m93f7Kq9ls31GgK52Z1rfnJMLv/tlx8loTtGVpUp7DK8bX7J/mnqvpChoPkJ3T3v09Ua23ixMX/taUfmBmHBXwpyZE1nJrw4O4+vqquV1UH9TCvyLKs7YvuyVyxx5jFd0nWfVBfyhTdazZorf23JE9Z37K6pFq3yHBqjR/o7h+tqlsneVB3TzIutFY7Q/FvZQj3985wVOwxSV7R3S+YoNbVM3Rbvk+GAHdykj/o7m8su9ZCzStlmATq0Unu1t3bp6q1KovjaLr7ZmPY/8vunuo8dStRVW/t7nussN7JSX52FS0FVfXmVb0/447lL009xnCstbL3rKre1d13WUWtsd7bu/undv/IpdRa5fax5bb7Gmat3qUpumVX1SdycWvtRRlm939mj5O5LLnWXTKcKuoa3X3jGmaKfXx3P2GCWiubnXis94gMpzq6XZKXZmgZ/r3uXtr5Qhdq7ZNhfO3iPsiLe8IwMO4v/mKGLuDnreLg4dQWh4Z19y2q6r8keW13TzE07ApJQF2SdR/UN84w/X9l6Br7qVUe1ZzC2G3nyUle1Bef9uWDPcyUudcbjyR+9wO7u9+4h1fpcquqw5IckeQeGca8vjrJPy27m29V/XB3f2RXO0gT7RidnnEczcL2+IHu/rEJaj0pw9jkr2QYk3S7JMd099JPol5Vf5ThFCKrGPOXqnpRhtczeUtBVT03w4GgyXtFVNVrktw5yeRjDFf5nlXVMzJ0Zf/bKXcoF+r9fobZIicf87ri7WPLbfc1TFiUDGNBD01yRobvs1tn+Jy8665+93LUvOr6g6tVdZWeYIx+Vb0nQ3A7cYvug/xwhomKKsmbu3uqc7yuXA2ntnlohv2Ra/YSJ0m6jKE4Saabv2HcB7lthtMPTX0qxD/OMEno15P8Y4ZTEf16d7982bXmRBffJVkLoFX1lxk+QE8ab98vw3m7lm6jI7MTHq29ene/d93YycnGM1bVVTMc5btVFiZjmqIFdXzeN2bYmZ3U2BL9W1nNVOiPytCt5vFT7DAs+M0MrZnP3eC+qcarTT6OZsFjuvv5VXXfJNfP0BJ9fIbz8C3bKsf8Jasd97fKsZqrHGO4yvfsNzN0+/p2VX09E85xMFrlmNdVbh9bbrtfa8WvqlcleVx3f2C8/aO55GRGy/QvufQsuu/aYNlSdPen1+2DTDJb69jF9rdz6f2PqT6Hk+HMAV/OuF9QE81CXsMpSp6eYTb6fXPxZ8jS/6drmFDoFzOc6uiEJL/S3R9acpmVDsVZsMqhYffp7t+uqp9Pcl6GsP/WDJN5blkC6vLdobt/de1Gd7+hqv5gmQXG8Hb1JNer4STca5/Y+yf5L8usteBzVXWzXDxG4iEZJquZysuSfCTJfTPs+D0iySRHFMcjb8/OED4q0+70rWwq9O4+YsrnX6jzuPHnyrqmZrXjaNb+v+6f5PjuPqPW7SUty4r/ht/tsraKcV29wvM2d/dLx3HJN+7usyeutbL3rFc8x8Eqe/6sePvYktv96IfXwulY/4M1nM5sacYWsRtk+Py9bS65D3L1ZdZa8Omq+okkXcMEQk/MRPsESf46Q6+BByb51QzjGy+YqFaq6teSPC3Jv2fYL6gM+1pTnI7luCS/kWEyralPx3KTDC19p09VoLufNv5c9f/Za8aeGNcahxw9JtPN+nyl8ef9k7yyu78w0S7IvHS3yxIvGfrz/16GFrKbZBhvePKSazwpw1iPbyY5Z7z+iQxdeo6e6HXdNMMMwV9L8pkk70hy4IR/x/ePP88cf14pyVsmqrUzyY+saPs4bRV1xlp3TnJqkq8muTDDl9GXJ6x3pQw7DSeMl6OTXGmiWvtkmEX6tWOtX8k4ZGGCWmutpR/LsPN1zanexyQ/kGEH4g3j7UOSPHbC9+wuST6UYRhCMnQd+vOJat0iyZuTfHC8fesM46ymqPWzSc5O8onx9m0y9GzZq9+zDDuuj8wwCVSS3CjJHSfcPq4+fp8dO94+OMkDt8D2sSW3+/H5X5nhAOjdM7Qu/Z8MO7XLrHFUhhacr2RovXrrePm7JL8w0eu6Xobg+O9JPpuh9ei6E9U6bfx55sKyUyZ8z3ZO9Vo2qPWeVdRZV/P6GYa+3TjDQcMpahyQ5H8l2TFenpvkgIlf172T/EmGc87fe8I6z8rQYPP+DPtZ2/fE+7jy7WZPr8BWu2TozvP8cUN6/3j9OhPV+rU98Pr2yzCGYOo67x1/vj3Jj45fTudMVOudK/z7PT1Da98PjdvKdSbcPnYkufm4HW7L0DX1jyZ8bS/OMMHDz4yX4zNMvrCSv+2Er2ufDF3WrjXevm6SW09U6w1JHpbkjPH2vkk+MOFre0+GkPP+hWUfnKjWKRnGDa+i1mnjDstirUn+jqt8zzJMVPfCJB8eb187yakTbh+vztDVcS1cXS3J6Vtg+9iS2/343FfN0EL2uvHyG0muOlGtB0/1OvbkJcm7x58nJ3lAhrGGH5+w3luT7Lui1/asDKHqLuP32u2S3G6iWj+b4cDuf2ZoRPlOkrMmqvU3GU4vdtPx8rQMY/Wn/nvun4n35cY6106ybbx+9SQ/uIrtZU9edPFdsh4mj3jSbh+4HNevqm3d/e1kOFF3kuf3BF0dqupaSX4549jJuvg8TFOc2DxJjh27L/9ehoksrpHk9yeqtaOqXp3k/+WS58+aYuzCUePPSadC/+4Td+9c2EaOr6pJTmkzukN3//jC7bdU1RlTFKqqByb5g1x6HM0U5xv+TlX9e5JDxrGuU7ped7+mqp4y1r6oqqbuCr6ScV1Z7Tj2VZ5veJXv2SrPl5gkN+vuX6yqI8d6X5+qe3tWPM/BFt3u08OkRc8bL1P7uap6U4+zZddw6q+X9ATzYFTVQUl+LZeev+FBy66V5A+r6oAk/yPJCzKEkN+YoM6ac5K8rapen0vug0xxWps7jT8PXVg21Zj5P8zQk+tN3X3bqrpHkiMnqJMMn1UPXrj9jHEio0lU1eMzDD/7eobgvdYte6pzUv9IkgPX7YP81US1ZkFAXbIVD67fluS9VfXoDOcee8F4mcJJSd6d5AOZ7vxc39XdLx6vvj3T/cOv2T9D1+X7LK5Cphlc/yN96VkPr7qrB19OXxt3Xk8fZ4H711x8Xq0pfLuqbtbdH0+Sqrppptvp+9MMJwH/QI+HFKdSVc/OMNHDh3Lx6+kM2+ay/WdVXTcXj/W+c4Zzrk1lleO6VjmOfWXnG85q37NVni8xSS4cx/Ku1btZFnagl2yV28dW3e4zbu//M0NX88V9kCm+R9+R5D1V9ZsZxqQ+OUOom8L/y9CV/u8z8T5Id//DePVLGWbBn9qnxsuVx8uUHtvd5ywuGL+rp/Ct7v58Ve1TVft091vH79MpfL2q7trjKY7GyaC+PlGtZJh47Fbd/bkJayRJquplSW6W5PRcch9kSwfUPd6Eu9UuGcaqPTbDl91PJ3lJkmdPWO9eGf4Jz09y8wnrvG9P/223wmWjv+NUf9sMrYtXzRDAn5ZhfMaU28g9M3zJvi1Dt7Zzk9xjolpvTbLPit6zs5NcZUW1bpfknRl2jN6Z5KOZqDvxWG+V47o2Gsd+k4lqXT3JH2UYg31qhiP5U3VzXNl7lmGyuBMzzOT4R+O2+dAJt497j//LF4zbyblJ7r4Fto8tud2P9d4xfhafOX4HPD3JMyasd9ck38oQuifrdpgrwJi7VVx2sQ8y1ZwKb8rQ++0FGcZGPz/Jv0xU6zYZ5mE5N8knMwxtmvK78x8z9I5YxXv24Uw0x8acL86DumRVdVp3337xfEhVdUp3//QEtX4qw5iklyf5sQx94B/T3edPUOs3Mky28w+5ZBeUpZ8Pb9XGVohfyaW7Di3tlDYLsx6+PMnDc8lZD/+yu394WbUWau6X5Ovd/Z3x9rYMQetry661UPMqSW6Z4fV9pCc6vU1V3SFDF99TMnGXqKp6Q4YQ8NVlP/cu6u2bi/+GZ3f3t1ZRd1XG7XKf7v7Knl6XZVnle1YrPl/i2Dp857Heu3viFoOtuH0kq3tdC/sg3z0vdFX9c3ffbYJav5Rh6M3TMkz+dN8kj+7upQ/tGHtEHJyhEWDxM3+Sc0SvUq3g9HPj58atkvxxLjnEaP8kT+7uWy2r1kLN/TI0oOyT4eDaAUn+urs/v+xaCzX3T5Lu/vJUNcY6t80wz8Z7csntcYpzbb82yRO7e8ozZ8yOLr7Lt7Zj8q9V9YAMLZs3nKjWczLsOH8o+e7pUt6SZOlhJ8MssH+SYVbitaMaU/a3X6W/S/LPGY72TdUl9b4Zzkt6wwyzy60F1C8n+d2Jar45Qwv7WrC6WoYv95/Y5W9cDmNX5SdkOKLeSf65qv6y13VpXpI/yvC6rprpu0R9LUM36Tdn4i+i8XkvSnLWFM89Bz3xaT32hFW+Z939kQwzOq7EuDO5qnPKbsntI1np6/pGVe2T5GNVdXSGVtvrT1TrwUnu2t2fTfLKqnpdhonylnpam9GPJfmlDGMl17r4TjJ28v9v77zD7aqq9f1+iaAgVUXKVSBwEQSkJjTjVUC5olQpgqCIFRUJoKiAVxBUimKDqyIloCAIiFJ+CmgoISSUxDRCsYAoihWBSBPC9/tjzJXsc3KScMmac5+zM9/nOU+yV7LXN885a681x5xjfEPSCNv3L+pYi5RoP7c+0TZnJfr2DJ1NLNDn4JXAQ2kO0LT+WpXoC9wqaSHtONL8Q9IE4ISMwfCZxHy7RNnbK4C7JN1O3zlIjvrrQUPdQW2ZZN5yM+EQ2BTXf972lRm05hokdRx7eY4PpKTfEgYd2fPtOzS3Y/4VxdZz7iVNs53jgTqQ1l62f1RIa77vK+f3KukS4mHXNI/eH1jZ9j4ZtCbbHrno/9mK1kEDHbd9fgn9SqVSeb6k7JK7iUDkRGIO8mXbtxbSX9r2vzOc9x4iZbP1cw+g9UvbW/Q7NsX2lpn0sp17AK1tbU8qpDUZ2K75naV671tsj8qg9XPCF6KZfxxAlCO8uW2tpDfRdpbF/gG0BszAtH1TCf1uUXdQWySlUK7nKLAvUVz/CklfAv7D9lslbUhYh5+TQWsWsZNUhMJF4VdLepvtn2Y4d3+2lDTO9iMACqfiT9j+bAatxyVt0aRASdqSvKYB67uvi+8NyuTiC/xC0k62r8t0/rnYblZ+17R9b269XiTt6GxjO6eLdKXyvBhocbcXSHOQfW0fRWSYtO7o30/vNUSZ0aq2N5a0CbAbUfPdNtOJoPuvGc4N9EmDXTFlpDWsQIfhVIt6L0t/vUrSR4m2QLlLqA6RdHe/OchpbZY0dfCizgUF2/9WPtfxl9k+seP1FyTtkUkLYn7zIcK0K+vvzPZNCofs9Wz/QtKyhElqT1N3UFtG0g22S7i+NbVxY4FjbW+a6qCmNnUnLWv9mLhx30CBNEdJdwMbOuMFKmk2EfSKcLd9mkjRztayRNJU25v3Ozbfam1LWqOAi4k0c4jeq++0PaVtraR3HlFPe2t6vTVwkO2PZtCaTbnf2a5EOv3StkdI2oxIHWo9vSY5D06z/bikAwkDnm/YfqBtraS3KvAlYA3bOzeLXLZbX+SSNMn2tm2fdwFaLyHM6vq7qbc+CSv5O+usK0/BwQbAz3LVvEr6CjDWdvb0ZUn7ANfYni3ps8TP8Qs5agwl3Q9cRnxvd7V9/gH0imQDJa3rgR1zPjs7tG4i6hnPbJ5rku60vXEGrRuJOtc7yJTmKGl3YA8iyO7MepsNXNz2Alu6Dps5SH/sDM7LC5iDzHesJa2fA6c3GYTp53uY87Qh+grR+/2SdGhvwmX3uLa1kt5A6d65fmcfBD5EBOHrKpy6v5Pj5ziYqAFqy0j6IlEI/kOiOTGQp5Bf0h22R3XeXHKlcJZOc+zVonBJM4h+oU+n18sAk53BoCCdfyn6mhblNG+5O2n9Ph1ak0g1e464cW+SSzsnkqYQdU43dnzO5hqQtKw1A9iUmIh9n8iGeIczmKwlvZKLXJ8nnEUvzz15TvePewhDshOIdK+7bbfeo7rk7yxdi28gmrbfSkzInrB9QNtaSe8DxC7ci4jr5CKnnpcZtGbY3kTSaKJNyleAY2xvvYi3vhCt5YH9iO9tGOG2f7EzGKssKBso4+LuaYSZ0KX0nYO03jat8BykWJpjyTTYpPcSD9B+rv+xlrSmE6mv/0yvXwbclOmevy7hlr0GMQf5A/Ae27/JoNUsWjefseHMu/6zLF6XQtHPdSvCyTrrHGQwUVN826fJST+h41iuJsjF+u/lCkQXQrGi8JRyu+OijrXEBcA4SWOJ39v7CFOJXIxi3sr95pKyrdwDb8103gFJ6WRr03dXIkfv2mdtPyr1WeTOFWA9a9tppfkbts9Z0OJQS7zC9iWSjoYw+5GUK/XxSNIEQtKTZNz1Jtop7SNp95Si/QPg2gw6UPZ3JttPSHo/sTNxqqSpmbRw9KM+W9L6RDA3Q9ItwFm2b2hZrrnu3g582/YVko5vWQMAh5PuWcBZCjf8i4CvSboMOLHlCfRIMmcD9eNlhAlN55wjV1/vYj1ecwSiC2FPSbOIkphriAWow21fsPC3vWAmEhkDizrWBqcBE9O1DrAPYTrYOo6e6NtIWo64d2VzsLa9fK5zD0RKp387889BWu8kADztSI9utF9EvjnIoKEGqC3jQum9iSOJNJR106RhFeJm0xqSLrG9r6SZDPCByLgrdnym884lpQG+lKjlXRn6tH5ZI4dmmlDOZF6biBNtZ5k4L2jlnkzNnXOloQ6EpHOJHatZ9HV0zDEJu1PR4mB4Sq05jJg85GB2ChYPBP4rPQSXyqQFZRe5Sk4gmkyBRyRtDPyZmEjkoOTvTJK2JXaE35+OZX2Op+9ng/T1d6IW8EhJH7a9X4tSf5R0JuE8foqiZdWwFs8/l47J5cHEdXEasdPzBuCnwGtalLsTWI1MgVt/bGetO+3Hx4DvAhtI+iNwP3FttoakCbZHa15Jztx/It8C1062PyVpT6Ln8D5EeVOrAarmtZ9bRtG2pHMOsmybWg22v5cyMbZPeu/IleaePsN7kYK4JsCyfcJC3jZUuAp4ijIuvjdJOoa4Tt5CdEu4KrNm16kpvkOY9OGfQ0f/PaLPWmu9JyWtbvshRYH2fJQMStpG0hjgcCIYEeeMIwAAIABJREFU7ewd+xixQ3BGVwbWEipQx9stJN1le8NCWssS7ZV2Ij5n1xILCznSr1Yj0lLvsH2zpDWJdKxc9WpbEG7jGxMT6VWAvW3PyKAlYvI6wvaJkl4NrG779gxaHwB+RCxijCWaxX/O9ncyaBX7naU0x08QTpinSFqH2NnJlS76VaIlxfXAOZ2/K0n32l6/Ra1liSyMmbZ/LWl14HXOYIQm6T4i4DjH/eoKJX2zzZ+npBuItis91yJCqfWKOnq8Km87liJImmV7I0lnAT+yfY2k6e5rAtiGzkFE+7mRRLp+w2zgvEwZQY32K+lbn//7hfz3F6pxDbHgOYWO9jm2T2tbqzRNSUIhrWHEgmTnHOTsXpzbdVID1CGMBrZCb91wJ602X+tMdt0L0NyGmDi/luhzORx4PMdqqaSP2z697fMuQKvk99WTdbwAks4hnAezG5z0Kumhtw0xcZ67yOV8hjvfJlaad7D92pS1cJ0ztBwoSZqcP2V7jgoYF3XoDgOWy1E32aHxPqI2cz4Hd0krtlmPKun7tt+9qGMt6Awnaq6L7OKoh1tELGAO0nrLlHStz3AG86UF6J0E7Emk+G5FuAdf7Qz10EmvZPu53YiMgTUIR+S1iPr81n0wlMkwazAg6RRgXI4FtEqQJX2mkhdJqylahiwjaXNJW6SvN5EhLcRhx/+EpBXbPvdCOIPoo/lrYBngA+lYDs6UdJiky9LXoQpzoRwM9H3lCo6bOt5rJV3ZfGXSAkDSWpLenP6+jMKMJAfnA5Mk3StphqSZCrOa1pE0UtLlkn6ZtGZk1Jot6bH09ZSkOZJypdw+RwT5z9qeZfvOzEHV1rY/RqRF4TDpyNJyQNKqks5RmEAhaUNF3WYOxgMvlvQfwDgibfS8HEKSfiBphRQU3wXcK+moHFqJA/oHp5LGAbQZnCb6TJBTINl6b8j0PCtWipMC0XuA5dPX3TmDU0kjns+xxdTYQNJepHYsHV/vJUM7lnSvmp6yE7KSguGriJZ9I9M98Qlg94yy4yR9VdLk9HVaxvnWicTC5K9sjyDKjW7JpDVRUjEjH0mjJR2c/r5K29d9P24FfizpyfS8ni0py2KhpF0kTZX0cG6twUStQc2A8lvK/zeRFvIqYiWsqVt4DDimRZ1OngJmKmzDO50Bs6SWpXP/RvP61Y2VlKvu71tEzdi30ut3E73dPpBDrOD3dXym8w6IOqzQidrXVwHfIR6AbXMu8XsqUf9xIdFKIbtW/zpNRR+3rTJKXpcmmtmddYFnUtDR1LuuQr6f53kkd+L0+leEs3qOHtEDGRdNy6ADkbL/mKQDiFrJTxPpc19uU0RRn78sBerzFfW7TX1VM+kS8G+ivjEHEyWdQRm3/X2J38+NxPd1uqSjbF+20De+cH7E/OY6l9FusL8+sAuxs7hrx/HZwAdb1OlkdWCWwjix83fWaqq0o4XTae5oiWX78U7NDJxDlFjsm16/m7h/vWOB73jhPGP7H5KGSRpm+4a0G5iD0cB7FS1ZnmZe3XDrqbGSjiNSpdcnfnZLETXDr29bK3EasYgxs8Cz8+vEtVBCa9BQA9SWUQFjGoej7vkl00KA/5e+SvGEoqHzNEmnEgYTL82kNapfbcn1Civ2HBT7vhzNnVclnHwBbredrck5YZixFXBb0v+1os4lB7936q1WgL8V1OqD7Z9I+kxGicZZ91lJT5HXeOSbRCP6Vyrace0N/E8GHSjrTizNb1yUq4n6Uim7Yw/gDNvPSMoxYfkw8+rzO4O2x4D/bVPI9knASZJOsn10m+deCCXd9o8lnjF/hbkLM78ggsbWkLQBsQu9oqTOwGYFWt7VtH0FcIXKtmP5fCEdKLtwB7Cu7b06Xn8+4yLXIwpX3ZuBCyX9FXg2k9bOmc47EHsCm5PuV7b/lDGDCyIL7s5C18cfCmoNGmqA2j7FLOULBqc4WjUsA6xp+94Cku8mUtAPBY4AXk24weVgjqR1HZboKIxHck1mi31fXVi5L2mFfo+idchV9DUeyWEqcZyks4n0zaxa/SaWw4j7SbZ7Sf8d25zYvlDhHtk4WO9h++5McsXciYlA7mjgx7ZnpftH2y1YGs4Efkc46Y5XmNe1nupl+xvAN1SwPt/20SlNei36Zh+NzyD3ftv3dR5Iv7ccDOu3MPgP8pRXFd/VLBicNguuawHr2f6FwlQr10JQyZZYAE9KGm17AoCk1xP1rznYnciIO5xYVFuRvgs1rWH7AUmbEu7YADfbzrX4/2/bbhbsUhlETh4CbkxlJJ3zghxtZj4F/FTSTQW0Bg3VJKll1KPGNJJ2JRqnL217hKTNgBPaTq/pp1kkIJa0I5ESch/xIFoLONjt9/lr9JYmjFRMmNL8O5POdOAt/Vfu3bITYYfeqcAjwHuAjxNW6HfZPnahb3xhWmMHOGzb78ugdQHx++rT0iaTVuf39SwRjJyVa+db0QNyPnIEBSpkgpPOW8yduEPzpSkVsCiSXmS71R0QSTvYvr7fgslcMi3OnAzsR9TWzs0+yvGMUSFzn3TeLxNu0helQ+8kDH8+3bZW0iu5q1mMzhIS2+sqWn59x3n6lRclzafOJ4JFAQ8DB+W6Xymcx7ci5iB32P5zJp0xxOJIc7/YE/hujkUvSZ8E1gPeApxE9Jj/Qa4FtpRSPB+2W9/pl3Qd8C/6lRnl0BpM1AC1ZdSjlvJp52MH4Ebbm6djM21nKYAvHRArWvY0Tqb3uMVWPf103k7UZf42aY0APmz7Zxm0+vx+FOYP0zP+zopZoUt6me2H2z7vArSyXefdRlJnL7WXEJOWKbZbT3XsHxQo6lFnuuV2QSrvTrwtUUO2nO01047Bh21/NIPWqsCXgDVs7yxpQ2Bb263W1kr6vO3jCi8E3QtskuvemzSaNNhTibryhhWAo5zByTTp7kXUwgkYb/vHOXSS1qnAF4gduGuATYlWRK328Exa87WUGehYS1rTSCUkheYguwHNAt6Ntq/OodNPcwUA53Xm/gDwOaJ1lIA3EnOrczNozSDuT4+n1y8FJjlTexZFj9C58w/bP8+hk7Q2tn1nrvP305pse2QJrcFETfFtn+NLiim/IVPDs7YfbdI3G6kMOg3HEw+jGwFsT5O0dg6hVNP1YToeRpLOzDShPQ3Y3vZvkva6RG1v6wEqcI2ka+m7cp9Dp2F34Hu2z8qo0XBbmrCMJVp65LwWb5W0oTO2tJH0KYe5zukM8LlyJjMy252pgCh6k57apobmN8FpbiJZTHDc1+RkVtvnH4CvE8Z1Vyb96QvamW6B8yhg/mS72R34gMPMrQT3EcYm2QJUumPu05TjlCrJ2cn2pyTtCTwI7EOknLceoFLGkKmhWAlJ2s0fRRjkAYxJKbhZ/AAUjr3HkeYgKZXzBLfvlA2xMLO57X8krZcDEwnjwbYRfcul5jDv/t+ukHQEcGnOoLQf30nZcOcRO7WPZNT6haSdvIS1tKkBasu4YG8zFTBk6uBOSe8ChqfUmsOIm1ouBgqIc/Ftyrn4/rUJThP3Eb3IWsf2USlFbzTxUPhuzpV7YDfg65LGAxcTK5i5zBdeA7yZSOM5XdIPicbmv8qgNRo4SHmdCJtazMkL/V/5eZBIi20Nd8cEp6jJie0/9LtX5QrqSpo/Adwv6RoiCL4+88/yCcI8rn+td2uLMy5o7iNpgu3RkmbTN5DKXc/YtEh7G3CR7Yfbfo6qoCFTBzdJaha63kKUkFy1iPe8UN4GbOZob4Ok84GpQC7DunMp5+L7ILEg0zCbMOHJwVhiMbmZd+xBHid1iGvvWkkPE/OPy2z/JZMW6bP9GqKt2GSFu/R5mYLIjwGfkvQ08Az57yGDgpri2zIKM47TgdcSPf6GA4/nuJAk3U0hQyaFIcGx9E3fPNH2U5n0ziFMaT5DmAgdBixl+5AMWtPdry5zoGMtaX2bqHG9hJi07APcS+pD1mZtl6IH2EPN70hR07uq7d+1pTGA5lKEc987icDu57aztOvp0Nye2B14KWEe85k2J58KY475sP1AWxrdot+O7TCiPOF3tg/MoDUMeBcwwvaJabd2ddu3Z9CaTXInJgxBsj3QJV0GfJXocbwNca8aaXu/DFo3EvfDn9veIj1vTrH9xra1kt4yxE7jfsRO2dXAxU5mLi1rHTTQcYdrfdtaryEWIVe1vbGkTYDdbH+hba3SSDqJqPV7kshCWgm42vbWLWrsTgQbu5EyBxKzieuj9cXrwiUkM4A3NWUkkl5GpPnmSk2dZnuzRR1rSet7wOuAK4h7/+5EOcSvoH3jHYUfQLNIPt721DbPP4DeJsT8Yy/gQdtvzqw3nPgsfJMwrBNwTJtzuSWVGqC2jKTJxMP8UsKB8z2E61zr/UnVBUOmVCNh27MX+Z8XT6dYQCzpl8A+7uvie5n7mWi0pDVQTVdDq7Vd6VrczsmEKaWj3GJ71MLfudi6SwFvJVYW32B7lQwaLwcOJFaa/0Ksyl5JBFiXOhqQL67GCo6eky8b6N/dYg2sohZ0gTdj56u97gwKniWC0yxN29PizHPADrZfq+iveV3u6zE3kl4BfIPY0RdwHTCmSaFrWau4+VOH9srE93mA7VzuqUVIKZRHAWd21DPeabvV7IF03nWJifLTkt5EGCZ9L0dKoObVX98NPGZ7Tqr7W94ZjHBK7ET30ytlMLg/cDKRGi0i9fZo2xdn0ptE1EB3uvh+xR29WFvUGtDcp8EtGu+kBbRZzXxR0fZlQ9u3taUxgOZqxML/fsR1n2tRYRNijvN24OfAObZ/KWkNos52wIXt/6PGBrbvSff9+XCGvs2DiRqgtoxSMbOkGc0HQ9JE29st6r0vQKuYIZOkUUQaStOW4lHgfbantK1VGhV28S3FAlZls+wMp3O/lXgobE/UDv+QCEBaT/OV9Cvg+8BY2w/2+7dP217sxuOSrra9S0rtNX1rZ2y7tbYUkpodsHcAqzGvXmx/ImhsfYEr6Y5xtBRZ6LGWtH6Zdv2mdgQFOa/HlQlXx7kph87TsqQoitq77OZPHXpvJHYkdgbuAH7oFlucSbrE9r6SZjJw/XXrE0xJd9ge1e9azLVjNY1YrF6bWGi9Eljf9tva1kp6k3IENgvQKmnIVMxgMOmtTtShijBmyuJ0m7QGcvF9r/O1ZCmCpKnAFs0ud1pAmZxp8f8jxH1qFaIO+ofO6xsxHjiL2Mx4st+/vdv291vQOMv2B9Ncvz92BjPDwUStQW2fJ9Iq37R0836ISDXLwfGZzjsQ5wAftX0zgKTRRFCXa3VqJGGssjZ9DaBa17M9TlFXW8LFdwTRgmVt+n5fOXbI/iZpN9tXJu3dgb9n0Gl4L1H78eFcP78O1u/30FvOyfmwjeA0nWeX9Odi78Y+D62bACSdaLvTYOeq9CDMxUHErlgn7x3gWBs8k9Khmt/bKnRY5reJwqlyDPAqokZ/G2AS4UTettYqhMHO2vT9TLfudJvYqkNrC0m5jPFIizPTiJKEo5ynjc6Y9OcuGc69IP6edjaba3Fv4lmdg+cctcJ7Al+3fXqauOeiZP11SUOmYgaDCn+P8UTfznvaPn9/bE8DNlUZF9+RRHZa/37DOeZy6rwGHQZ2ueKOtYjFkWmZzt+Hzud0Wgx9dZPJ0kZwms7zwfTn9m2cb6hRA9T2eTdRy3UocATwaiIXvnVc0JAJmN0Ep0l7QqrzysWFRApWn75POUiT5v9m3qRvxzTpy9EE+SdEsH8Vmb8v4BDgQklnpNcPEtdnFpyh5m4hXCjpEMKMZgph1vFV219uS2BBaTUNmdJrVpG0ju370hhGECvCrZJS2N4FjJDUWUO2PNB6amrim8CPgVdK+iKwN/DZTFpjiN2PW21vrzB1ydUz7grgZuAX5DNHAuZOnEsZ4wFsmnOyDOBUomL7AUUbnSbl+3Zn6v9LmI58F9hA0h+B+4mSgRw8kz5vBzHPOXiphfz/xeVIYlF8jqQnyVh/TQFDpg6KGQwSi++jCQO+dYjP2/gcmSUAklYiysHWBl7U/Aydx7292NwKuE/SYUS9N4Sx1X05hJwcliW9kr5ZM7/PoafwA9iNmDNOIzYEbrJ9ZIsaCzXJco/XudYU3wwojCXWtH1vZp2ShkxfA5YlWpaYSKX4J8k6v+3JupIDYpvnXIjWTwkjlexNkCXd5hbNKp6n5nLEZz133XDJ63Ga7c0kHUC0NPg00b+ztVXgjrSalxApetOJyd4mRMpX69dnSpP+LvMe4msTO9LXtqyzFpEidxJ9nSlnAzNypGUn3Q2AHYmf4zjbdy/iLS9Up0nhnAZs7aj/y5bCmeO8C9AqZoyX9F5CGNNsRN9JX44+qPsCXybKAwS8gdi1vaxtrQ7NlwLDct4bFb1qDyHq0i5Ki07vtH1yLs1SqIAhU4dWMYPBpDecWCzZnvj9PWl7gzY1OrQmArcy/xwkh0FYybnVK4mFyR2I39k4Ypez9YUFSbsSZnVrEAsXawF3O19v46m2N0/ZOq929I2e0fIcpPEseSWwHdG7FlIZle0cLs+Dhhqgtkz6kHwFWNr2iFRbcEKOFE6VNWRaWD1m67nwirrQ/YkbWmd9besrRm3fVBah9S6iLu46+n5fQ77YfQHX43/aPnahb3xhWrOI+usfAGfYvilXPaOki4Ev2p6ZXm8MfNL2e9vWSud/MWEEAhnTzZPWfP1dJb3J9o2Z9FYmsko6U8tav/YVbQ0OBg4nJkf/JFzAW6/7k/QFYKLtn7Z97gG0ihrjJb17iN32E4ADiEnfmIW+8YVpTQfe0kxeU+r0LzJ9pvvsWDXHM+1YFUfSbnT09bZ9dQaN0oZMJQ0GxxG70JOI7IgJGXfz59bn5zp/P61ic6uSpPvHDsQ9Y3OFu//+tj+USW8mYeJ5PnCs7TtyzSUlXQ18sLnvK+qj/7fXA9Sa4ts+xxMriTdC1BZIWjuXmO3fSBruaKY+Nq3E5dApnQN/MDFJX4p5K4oGctxEf6ZyTZBfR6TZ7kDf76snit1LXY/AmcDviF3N8WlHMFcq4gZNcApg+8608JSLph76JURdUrYaQ+ASRduBLye9U4nFhRzukScS9a2/ZZ4ZTpZr3/ae6a/Hp8W1FQkTlxyMAY5RmR51rwDuUvTcy2qMl/hP2/tI2t32+ZJ+QBj95GBYvyDgH0S5TA5+ygA7VjlQOLIez7yav+b6aM1krZ/eycTO34Xp0BhJo5sUyLZI9YSnucOQyVGjnKNOGdsH5zjvAphBZOZsTBhCPqIwn3py4W97wXxf0geJNk6dn+vWnOI7KDm3Kskztv8haZikYbZvkNSKH8UCOIG4F05Iwek6wK8zaa3db1HyL0Qv+J6mBqjt86ztRzPWYXRS0pCpNJvafl0hrVuBH6cV4dwTzD2BdZzJHr/LFLsebX+TSB0CQNLvibSXHNwt6WzC+MNErVqu1NTjgDcBGxKT6J2BCeSrMdwaOAWYSNSfXgi8PpPWvsC6pa99Z67Vt738ov9XaxxfUAvifggxQd8Y+DOx65iDayRdS5SRQJSR5NqVfkmbtWKL4BzCj2IKmWuUE28DNrP9HICk84Gp9E3lb4uShkzFsH0EzC2POZioSV0NeHEmyX8Ti4TH0nfxLsciRsm5VUkeSb+v8YRHxV+J1mlZsH0pkS3WvL6PTH4zwI0d90YTmWpDusvE86EGqO1zZ0rjHK5whj2MmPzloJghUxe4daD0w0ycRuwYzSzwkJ1O1OlkSxfqRNJ2zJ/GlivY6dr1mH5vuR5GBwMfYZ7b6HjmmT60zd5Eq4aptg9WmMacnUkLIgB5EliG2EG9v5nYZuBOCl77JZH0H8zvipnDfflttj/dT/sUIFcQ/t2Ulv1Zoj3KcsDncgjZPiqZgowmFgm/a/vHObQou2P1qDO1QlkIKxGtSiCyB3JR0pCpGJIOJWqgtwQeIFrs3bzQNy0eRxLZCjld9huKza0kjbB9/6KOtcTuxLPsCKIUYUVil3PIY/tQhVN2k7af8944aKg1qC0jaVliFWwn4mZ9LXCi7acy6RUxZCpNMgNZl3BXfJp5D74c+f3XAjtnnJh3at1ImOzcQf7etQM6fuaqs0r1R092rNwPB15s+4kcer2IpNttbyVpCrEjPBu4M6PRw3TChfZE4OVE6vQztvfOoDUyad1JmfTUIqQA8Z3AXfT9nOX4TM9Xq1ayhj43klYjdvWfA+7IUcuYdD4GfBF4hI4dqxxptynldjiRQpndd0DhGHwyscMiYlJ7tO2Lc+iVomSwI+koYiFyijMZxvXTuxLYr8SzsvDcaqD71RTbW2bQGgE81My109x4Vdu/a1urUoYaoA5hShoyJb1iu3GppnA+bD+QQes8IpXmZ/SdQLTeZkbR9H4+cqQhqrzj563Am23/K71eDrjO9nYl9HsBSd8i+v/uB3wC+BcwLVf9laSRtif3O9ZKk/EBtGYRAXB/p8qS7bJaR9K9wCbOa2b1EaJFw7pAZ6uN5QmDpgMy6X4JONX2I+n1ysAnbLfeHkjhhvk5wqlSwBuJ59m5GbR+S7g7Z9+x0sAGg3bLxoL9NFcn6lBFOI5nCfSTVnZDpqRTLNgpjcLUbSNiUaFzDtL6YnKJuZXCrX0jwtPgqI5/WoFw5m59wVVh0rhdU0KSyo1usT1q4e98wXold4eXSGqKb8ukXYJjmD+Qy7HCfTyFDJkWtBtHptq4HIHoQrg/fS2dvrLhcJst1evvTqJupojjJ1HX9a/mhe1/pYyCLBROX86OonD9pBQMfEfSNcAKTs2/c2B7sqTRhPv3WEmvIGpec/D3VDvca9xHGI5kC1AJt+qfMUBboExpqQ07u8MV3vY/Jb2NPP1rjwI2t/0PAEkvJ8pjWg9QgVlAkcwOFzYYTM/q8cDNtu/JrJXdkKkj2FlRfftCrkBH66Mhzk/SV3Yc/YY3JVKYIa6T6S3LrA/sQqSa79pxfDbwwZa1Gl7U6W9g+98pSM3Fj4D+zsuXEWnhlRaoAWr7lGyCXNKQaSQFd+NK4o5+p8koaTlnak6v+Xv9nS4pV6+/0o6fj0vaokldk7QlURPSOqUXTEpg25J+QnrAlUhNSqZMI4kJxVhigeYC8hglTVH0TbySHmixJOl04pp7gjAG69+2obXdD9uPAo9K+gbwsFPfTknLS9ra9m1tafVjuKQXN7vDKW0ul1HMg8QEtmE28IdMWnOI31mJHatVgS8Ba9jeWdEXdVvb57StlRhL1PGernAWnQaMt/2NDFolDJm6EewUxR39TlOWwqtzLUxKGkP83BrX3gskfdf26W1p2L4CuELStrYntXXeRfA3SbvZvhJA0u5A6xkSS8iCyaCgBqjt87fmA1KAkoZMpXfjiqFonXAIMWmZQtx4vmr7yxnkjgVGuV+vP2LlrW2Oz3DOhXE4cKmkP6XXqxO1eTnIvmAi6Srm1afNR6ZA/1ZJo2zfkeHcA7EnsDnwSwDbf5KUy5V28/TnNh3HhnKLpSY1egoRdHeS67r8Nn1X7R8f4FibXACMU/SgNPA+ou9fDv4I3CbpiqS1O3C7pCOh9ZKLYjtWwHlE0Nj0g/4V8EPC3bd1bF8v6SZiZ3N74tm2EZAjQIXMhkxdCnaKkrwpdiPm5NOIYOsm53Gafj+R3v540j6F6PfaWoDawZ6ptONJos3XpsDhti/IoHUI4d57BrH4/wei13HbFFswUfRaHehZkq1ueDBRA9T2OU7RkqJEE+SPEw+9pwn76WsJs5MclN6NK8mGth+TdADR1uDTxKQzR4BarNdf6do+Ry+wDYgbuIB7bD+ziLe9UEosmHwl47kXxPbAhyU9QAQfuR9E/047t4a5RldZKJ3qmJtm10PSmP67U2mXIgfqXJRx9KLM9hy3fWqaJO1IXIsn2s7VB/W36avhivRn6wsmnTtWBXiF7UskHZ20n5WUrd1M2sl/KRF03EzHgmgGTgKmpp3ouYZMmbRKBjulWTHNQT4AjLV9nKRcpR2ib7ujOelYDnay/SmFA+2DwD5EnW3rvzPbvwW2Sd4XarJMMuiUXDDZJfP5BzU1QG2fYk2QHY5vxzJvZTYnxxfQ6BZLSVoK2AM4w/YzzYQ9AwP1+svSgkDSNsSq6GuJ1M3hwOPO2wJgFPPqQjeXlKsuNPuCSZfMe3YurHeJpDOBlRRtN95Hy21tJB1o+4JmJ6w/Le+MdYODmH936r0DHGuD+yQdxrw2Rx8lamCz4WiRkr1NSmepRW4k7UIs5jatgXK2R3k81dM2i0DbAI9m0GmYQZQJbJx0HpE0yXbr5Ra2L0q7f40h06czGjIVC3a6wIuSsdW+5J/PjSUyFZo2JXuQaTefmAdDpIJfZPvhXCVpkl5MtLVbm/h5AmA7V6uZ7Asmhb1YBh01QG2fYk2QSxoydWmyXoozgd8RPUrHJ5e7LDWoLtvr7wzCDfZSIiX2PcB6mbRK14Uen+GcA5LS508CNqSjxsQZWlKUfiDZ/oqktxDX+/rA52z/vGWZZlc2V+pwV1C083gXMELRJqJhBSIzIgeHAN8kTIpMZOp8KJMW6V51CvBK4n7VE30uga8D76BM7+sjiRTwdSXdAqxC9DvOgu0jYK6L+sFEQLIaGWqHSxoyUTDY6QInEBlwE1Im0jrAr3MI2f5qWlRo5iAH256aQwu4UtI9RBD30VTSlKXlIpFx8SiR/ZbTsK6h2IJJlzYbuk5tM9Myks4CvuYyTZDvZQBDphyT3CXpA6J46g13hv5nKtirS9Jk2yPV0SdR0kRnavuiwm1tSiFpAnAc8DWi5uRg4t55XFcH1gKSTrH96UUda0FnOHCY7a+1ed5ukhayRjCAsy4wI8f9ozSSfgPsavvubo+lTVJK6o4u0Ps66b2IeaUP92YsfUDSoYRD65bAA8wLIK/PoLUDEei8gWjVls2QKRms7UkEO1sRNYBX2966ba1eJs3lZnUarRHP7VaN1hSGk9sAdwOP2Z6TSkiWz7HLLulO2xu3fd6vZJaMAAAeYElEQVSF6M2yvVGa8//I9jWSptveNIPWZObfbPhP2yWyJ7tGDVBbRmWbIE+wPbrt8y5Aa6APyHruaEFQWTQq2KtL0njgzUTK5p+Jes335riBJr1LiSAku5FWyQUTpV57kmY22RGSbrb9hkW9d7CjgXsLzsh0v7qh1+pQG1SodZSklxAmJxvRdzf/fZn0brGdw9G5q0gaRaT43kT+3tfDgbczf6ZTltR2SUcRQemUEosk6fvrNGR60vYGLWsUDXZ6GUlTgS2aheT0s53c/znQktYk29u2fd4FaH0XON32zEJ6xRZMSm82DBZqim/7vLWgVklDJmz/RtJw23OAsZJyOQb3MiV7db2bMGA6FDgCeDVRo5GLkkZaJdOXn0oP8V+n3Yk/EimPQxZJHyHqF9fpZ8axPHBLJtmJCofFHxIGUMDQbTPTIGkfwlDrRvK3jvo+cA/w30Ra4AHEpD0XkyX9kHC8zfqMkfQaorZ2VdsbS9oE2M32F9rWAr4I/IsI8rP2vgauItIaS7Sew3nc5wdEhQyZHGZgp3UGOw4X2scX8rbKwJQ0WrtO0l7A5QUyq0YD75VUYnNoGPG5PpV5CyZPEM7jOXgizROnSTqV2GzIZmg4WKg7qEMYSRcQhkyz6DBkyrGaXno3rleR9HNila+zV9dhtnfMpLcMsKbte3Ocv5/WGwc6nqN+ueSKYtptuZtYIT2RqDE8te2UqJJIWhFYmQHSU20/PPC7FlvzhgEO2/ZQbTMDgKTpwFvcr3VUplSvqbY3b657hbnbtbl+hor2Mv3J9Yy5iShZOdP25ulYlrS95v7R9nkXoJUlI2EwIOlrRCrx08TC1nggiyGTpM8TBlAlgp2iSBph+/5FHWtJ63JiMa3TaG1723tk0JpNBFJziJ3GbDXsqeRiPnKUvCW9krvDawF/IRbTjiDaOf2vw7m4Z6kB6hCmM+WwgNZAH5Bv2f5NCf3cSNqO+VOwWjf3kbQucCGwRjr0IPDuHDcaSbsSOztL2x4haTPghEw7mo1mqVTHYgsmkvaxfemijlWWTPrfh9Pq+vQc92ZJt9veKl3/HyWu/dudwbCrNJLusD2qCcLTsWm2N8ugdTJwve3r2j73AFqnAONKaHULzTNk+iSwmu0chkzFgp3SLKDUYortLTNovZIwWtuBeUZrh+d6VpdE0qZEPTRE3fX0jFrFFky0gFZm/Y/1GjVAHcKooCFT0iu2G1cSLcB91vZhGTWz9upKGlOIh9CNHRO+bKv5kvYlesfeSEwe3gBkSXUsuWCygMnDfMcqCyctXnwJWMP2zpI2BLa1navFQREkfRnYhL6to2a4ZaOppPUB4EdJbyywHPA/ts9sWyvpvYqo9X49MZmdAIyx/WAGrZ8R5QiX2t5C0t7A+2233nqpI9h5GniGvDs7exLOnsNya5VGBQ2ZehFF3/CNiFTRozr+aQXi2blRVwbWIpJ2I/rjQsxFrs6kMwb4IPNaOu5JdEk4PZNeyd3hgeYgcxfyepUaoA5hVNaQqfhuXCnUu+6zt9neut+ORM4AtViqYzp/1gUTSTsTLQ32JeomG1Ygrpetcuj2KikAGQsca3vTVPc0tVQWSE7Ut3XUeOdrHVWUVJLwA6L2FeBA4ADbb8mgtQ7wXWA74J/Ec+2AXCl6pZB0H9FrskRLm6KovCFTkWCnFKnEZw9gN6IVUcNs4GLbQ9rnI2UqjCKyxgD2J66Vzyz4XS9Yawax4Pl4ev1SIt18yKbXa14rs9FEjXfDCsCztt/clYEVopokDW1KGjIdTziV3Qhge5qktQvq5+ROok9cdvfZwtwp6V3AcEUvz8OAnA+8Yf3ShP5B7Bq0TueCCZBrweRPwGRi8jCl4/hsYte28n/jFbYvkXQ0gO1nJc1Z1JuGCLcQu2MGbs8lIunlxL242dG8GTjRdq6+q6vY7qxDPU/S4Zm0HrD95jSxHJY5u+Qy4FzgGudvNfNr4M5eC06huCFT/2BnjKTROYKdUti+ArhC0ra2J3V7PBl4G7BZ8xmTdD4wlb6+B20h5mXAkf6etVFugQWTicS89BXAaR3HZxPpxT1NDVCHMIVXlp+1/ah6pzF2JyXdZ0vyceBY4nu6iGgEfmJGvWskXUvfVMefZdI6nswLJql+ZbqkHxMtbOYATVuF1muslgAeTwFW095gG6Kx+pBmgNT2nC6+FxM7Vo0b9wHE7n6ulfS/SzqQeZ/p/YmFpxzcL+ka4vvJnSL6HaJm8nRFe6zzbN+TSesh4MaUQZC1pU2PUzLYKc2ekmYRqaLXAJsSdaEXtC1U0pApsRLQGO+tmEkDIjvntvS8htiZzlY+UmLBJM3xH5B0APAn208l7WWAVwG/a0trMFJTfCvPC0nnEMX0nyEmR4cBS9k+pKsDawEVdJ9NekUMmbpBqVTHkunLkm4F3mz7X+n1csB17vEeZG0jaQuinnFjImthFWBv20N6JbhkavtAxinK6EgraU2ipdO2xMLCRKIGtfXF0TTp2pVoH7UFcDWR5jihba0OzRWJoPtY4A/AWcAFtp9pUeO4gY7b/nxbGksCKYXzTU4u45JeRuxaDdkUzobGDCzVK+9BZOjckOkeUtKQaX/gZOAGYk7wX8DRti9uWyvpbUHf+cfUHDpJawZ9F0yGEyUrOeYgk4HtnFoUKlrO3GJ71MLfObSpO6iV50vp3bhi5ApEB2JBhkxADsfgkcAxzB8M56pBHQH81KlHoqRlJK1t+3cZ5EqmL7+kCU4BbP9L0rKZtHoW279Mi0HrExOIe9sMBLpIsdR24AZJ+wGXpNd7A/8vkxa2f0+kuGfH0ZrkEuASSSsD3wBuAobn0Eu7+QcS/aKnEjsho4GDgDe1pVMD0dY4CZiqaFc1N9jp7pBaY6n059uAi2w/3Ha2Woch04ppIblhBaIfcOvYvkjSjcROo4BP2/5zDq2UkTPLqa+2pOUlbe287eBK7Q6/qAlOAWz/OwWpPU0NUCvPC9tPEAHqsd0eS9ukG9vpwGuJmsbhREpnDpfFkZQzZLqQcAYs0iAeuJQwOGmYk47lWOUruWDyuKQtOh58WxKpWJX/A5JeQrRGGU2qn5T0nSZtaQhTMrX9w8CRhCssRCD8uKQjyeAgmdIox9h+JL1eGTjNGfqgpvO/kfj57QzcQRiU5dC5nOgh/n1gV9uN/8AP025FGxpft324pKtIae2d9EAJSVFKBjtd4EpJ9xDPlY+mLIy274vrA7sQQdWuHcdnE+63rZMW5Btn51wp9A3fJjIvGh4f4FiblFww+Zuk3WxfCXPNtf6eSWvQUFN8K8+L0rtxJUkTkv2IYGok8B5gPdvHZNC6FDisY0KUDUkTbI/OrdOhN1/PQknTc7n4lkLSKKL270/p0OrAO21PWfC7Kv2RdAkxGWqCq/2BlW3v071RtYOkvQjjol5z8Z2vlcFAx1rSup/ILLkEuLJx48yBpB2cuRWKpC1tTyldQtKrFA52iqHom7wNcDfwmO05yShs+RwBeElDJkk7EAuSbwDWIT7f452hf+cC5h/Zuhak86/OvAWT2zLuDq9LbDiskbT+ALzHGdrqDSZqgFp5Xki6lwF24wobNWWhqeHqvJlJmpijxjCttm1GOH1mNWSStCMRBIzrp3X5At+0eHo/B07vt8p3mO0dM2iVTl9einmpqff0SGpqUQZarOiFBYwGSSvQ91p8eCH/fXF0NmH+6z7XZ3o6Uff3z/T6ZcBNztAaSNIKth9r+7wL0NqHcPCdLemzxC7LF5osicrgo2SwUxpJk2xvW0jrVOALFDBkSnrDiSBue+AQ4EnbG2TQuZwwqvt2OvRRYHvbe7StlfSKL5gk/ws5o8P5YKKm+FaeL39rAo8e5ImUzz8t3bwfIhow5+D4TOcdiIOJNLalmLeoYOY1sm6bQ4ALJZ2RXj9I1HfloHT68vrAhkStzuaSesbYqiBTJW1j+1YASVsT7VmGNJI+DJxATPieIxYxTEyi29Y6F9gEmEWZz/RpwERFWxYTKbdfzKT1b0kfI+rk5tbEZUon/h/bl0oaDfw30bLq28DWbQlImskAqb0NvZB9VBLb10u6ib7BzkZErfJQ57qUhXF5gfKfnWx/KhkyPQjsQ5gY5XAMHkfMpSYRLbFG9avXb5NDgG8CnyU+d+OAD2XSgnANHk04gWdfMJH0dtK9salPtn1CDq3BQt1BrTwvSu/GlUTSWsBfiPrTI4hi928N9fQJSTNz7HQ8D93sq3wl05cVLpxvIgLUnxL1cRNs711Cv1eQdDcR6P8+HVqTSGt7jqifHJITdkm/JhrEZ68JknSX7Q1z6/TT3BDYgQi8x9m+K5POpcA9RGP6E4gWOnfbHpNBa6rtzSWdBMy0/YO2U5fTcwXgY+nP76c/DwCe6PXJZdsMEOxMyBjsFEXSbOJ7m0MsdIkMNeVJa5btjSSdBfzI9jW5MlkkfQ3Ykpgz3kLsOE5KhmhDnoK7w98Blk06ZxPmeLfbfn/bWoOJGqBWnheSLiB24/qs3OcyyyiNosXBmrbvzaxTzJApPYC+lmtC2U1KLpiknZBNCQv5TSWtCpxte9dFvLXSQceEfUCGarmAonfnO5KRXG6tcwiTol78TDdB4wzbm6S0+mtt75BB62rgj0T/2Mb07PZMk/RbbL9+UccqC6fXg51SpEWZPYlrfivCNOlq261lDwyguRyR0fVJYDXbQ76PeMkFk457YvPncsRu+0459AYLNcW38nzZtBu7cSWQtCuR4rU0MELSZsAJmVwWz2AAQ6YMOpBaJiTzkaeZtyo7JHeq+lEyfflJ289JejbVGf6VDOmbvc5QDUCfB0cTabC30Xex5LAMWucDkyT9md77TDd13Y9I2hj4M1Frm4N9gbcCX7H9SDI7OSqT1ksljXbq56rog52rhKRnsX0E9Al2xgKrAUM+2AGQtBvhBAvR3/XqDBrDgKuAU5lnyPQEsHvbWknvUKJmeEvgAeBcIpjrBWYQ39fGwKPEfSvXgknj6PyEpDWIVmYjMugMKmqAWnm+3Cppw15cuSfqQrciCuyxPU3S2rnEbP9G0nDbc4CxknL173xrpvMOBkoumEyWtBJwFjAF+BdhclWpAJwJXE+ZeuhzibruUrXXJfmuoo3NZ4ErgeWAz+UQSrvdl3e8fojwHsjB+4FzJa1ILKI9CvRE5lFJejnYkXQykSp6YTo0Ji1qfKZNnbTQelqnIZPDLTuXY/YywFeBKbafzaQBgKQRtu9f1LG2KLxgclWag3wZ+CVxHzkrg86goqb4Vp4XqX5sXaDnduMk3WZ7684aJGWyJ5c0nkgrO5vYIXgIeG8POZlux/wOo62bCZVKX1a4EbzK9h/S67WBFWzPyKlbGTook+P3ArSuz5HyWslPyr6Q7Ue7PZahiKSjiLTe7MFOaSTNADaz/Vx6PZwoKckxB/k8sftXwpCpGJJ+aXuLfsem2N4yk17/BZPG0bfV9lVp13sb2xPT6xcDL1kS7iN1B7XyfOnl3bg7Jb0LGC5pPeAwINeu5ruBYcChhCHTq4G9MmkVJdmur0u42c1Jhw3kcLstkr5s25J+QjyEsP27Ns9f6QlukPQhInWuM8U3R5uZeyT9YACtXjCr+xJwqu1H0uuVgU/Y/mx3R9YOLtRCp1ex/eVujyEzKwHNPWPFjDpHkgyZJGU1ZCqBpA0Id9sVJb2j459WoMMNPANFdoebXW9g2/T6aTru/b1M3UGtLPFIWhY4FtiJuFlfC5xo+6mFvvGF6xUxZCpN2mXfsMSq7IIMd3LUOUr6X+A823e0fe7K0CctkvTHtnO0mRm7AK0hnzI6kIvuQLsilUqvIWl/4GSi3YuIWtSjbV/c1YENART91vcAdiNKAxpmAxc3O49DmV7d9V4UNUCtVArSachkO7chU1FSm4jDUj1XzyDpLuA1RBrP4/RQenulMlhIaY6j0g5Bs5A32fZG3R1ZpZKfZNQ1ini+3Gb7zxm1shsylUbStrYndXscOehoQ/QsYZg0pHe9ny81xbeyxCNpJHAM89dO5ghAjqegIVNhXgHcJel2+qYfDsngu8NgYeduj6UyeJG0D3CN7dmSPgtsQWRgTM2g9SqiTdXrifT5CcAY2w+2rdUFLgDGpV1iE0ZC53d3SC+cfumG89ELadmVdkjlMU0N4z2ZtYoYMnWBPSXNItrnXEO0hjvc9gXdHdYLR9Lrbd8CrJIro28wU3dQK0s8ku4lWgz0ccbMlC5azJCpNJLeONBx2zeVHksbNAYLksbZ3rHb46kMTjp6040GTiIyJI7J0VdQ0s+BHwDfT4cOBA6w/Za2tbqBpJ2BHYkdgutsX9vlIb1gFpCO3dATadmVdpC0A+Gr8Aaihdk0YLztb2TQKmbIVBJJ02xvJmlPIuX3COCGoWxA2TEHWSJLHeoOaqUCf7N95aL/WyuUNGQqylANRBfCMEnHAa+RdGT/f7T91S6MqTL4aAzB3g582/YVko7PpLWK7c7A5zxJh2fSKo7tnwE/6/Y42sD2wd0eQ2VoYPt6STcRO5vbA4cQxj+tB6iJUoZMJVkq/fk24CLbD4cJ/5DmmbTQ9SpJ3+z/j87Ta3vQUAPUSgWOk3Q2MI78zpgfJwyZngYuIhkyZdApjqRtiPTD1wJLA8OBx4dwncR+xErsi4DluzyWyuDlj5LOJNpHnZLaAAzLpPV3SQcS9w6A/Ymm7UOelBJ7CvBKYge1Z+qsJL2dCDjmuoraPqF7I6oMJiSNI2oMJxG9XUfZ/msmuZOAqZL6GDJl0irJlZLuIVJ8PyppFaJecyizC/Fc2YHowb5EUVN8K0s8ki4ANgBmMS/Ft6Zg/R+RNJkI6i4FRgLvAdazfUxXB7aYSNo57exUKvORXMDfCsy0/etkdvI629dl0FoTOINoOWAi+2JMjnKE0kj6DbCr7bu7PZY2kfQdYFliZ+xsYG/gdtvv7+rAKoMGSV8jWpk9DdxC1KNOsv1kJr1ihkwlaHqFAncDj9meI+mlwPJD/XsDkLSp7endHkdpaoBaWeKRNNP26wpplTRkKoqkybZHdtbUSppoe7tuj61SqQxuJN1i+/XdHkfbdNQoN38uR7SL2KnbY6sMLtK1cTDwSWA12y/OoFHMkKkkkibZ3rbb46i0R03xrVTgVkkb2r6rgNaFDGDI1CM8IWlpYJqkU4GHiLSlSqXSApLOJ3ZMH0mvVwZO65Fsj8mSfgj8hPylFiVpdsGekLQGkZI9oovjqQwyJB1KGCRtSbQzO5dI9c3BWMKQ6XRJWQ2ZCnOdpL1YwnqF9jJ1B7WyxCPpbmBd4H5iYpStz6WkCbZHt33ewYCktYC/EPWnRxDmC9+y/ZuuDqxS6RE63b8XdmwosgDX2yFfaiHpf4ja/B2B/yVSs8+2/T9dHVhl0CDpKGJXc4rtZwvoDaevIdOTtjfIrZuTjl6hc4hFoV6qYW9a3i30WK9RA9TKEk8KrOYjU5uZHQljkxKGTMWRtAywpu17uz2WNpG0HfOnZX+vawOqLJFImg68yfY/0+uXATeVKlGo/N+R9GLbTzd/J4ySnmqOVSolGcCQaUJGQ6ZKCwzUZqZpQdOtMZWgpvhWlngKG4wcTBgyLUWHIRMw5ANUSbsSPSCXBkZI2gw4wfZu3R3Z4pFqdtYlUqGaliIGaoBaKc1pwERJlxHX4L7AF7s7pHaQ9Cpip/H1xPc2gUhnfrCrA1t8JgFbAKSg9GlJv2yOVSqFmUGkEm8MPAo8kuo3sxgylUTSboQrMcCNtq/u5ngWF0kbEO7fKyaX84YV6HAE71VqgFqplGXTHt7tOB7YCrgRwPY0SWt3bzitMRLYsNa1VLqN7e8lt+wdiBS2dxSqnS/BWOAHwD7p9YHp2Fu6NqLFQNJqwH8Ay0janPh9QUwul+3awCpLNLaPgD6GTGOB1YDWDZlKIulkIm35wnRojKTRtj/TxWEtLusTrWZWAnbtOD4b+GBXRlSQGqBWKmUpachUmmdtP9oDzbH7cyfxAH+o2wOpVNK9oxfvH6vY7qxDPU/S4V0bzeLz38B7gVcRO9/NjfExwsm9UilOYUOmkrwN2Mz2czDXUG4qMGQDVNtXAFdI2tb2pG6PpzQ1QK1UyjIaOEhSdkOmLnCnpHcBwyWtBxxG9Gkc6rwCuEvS7fStGx7SqcuVyiDj75IOBC5Kr/cnHG+HJLbPB86XtJftH3V7PJVKYhngqxQyZCrMSsDD6e8rdnMgLbOnpFmE+dM1wKbA4bYv6O6w8lJNkiqVgpQ0ZCqNpGWBY4GdiMD7WuBE2091dWCLiaQ3DnTc9k2lx1Kp9CqS1gTOALYlalAnEjWoQ/reKOlLwKn9WgN9wvZnuzuySqV3kLQ/cDJwAzH/+C/gaNsXd3VgLSBpmu3NJO0J7EF0SbjB9qZdHlpWaoBaqVQqlUqlkoEFtAaaz5WzUqksHpJWJ+pQBdxm+89dHlIrSJpleyNJZwE/sn2NpOm9HqAO6/YAKpVKbyBppKTLJf1S0ozmq9vjWlwkbSPpDkn/kvRvSXMkPdbtcVUqvYSk8yWt1PF6ZUnndnNMLTE8tZcB5rbiGtKGNJXKYCO57e8C/Mr2Fb0SnCaulHQPYdg4TtIqwJDOTHs+1BrUSqXSFhcCRwEzmddCpxc4A9gPuJR4QLwHWK+rI6pUeo9NmjRYANv/TO63Q50LiEnlWCJ1+X3A+d0dUqXSc4wlPD5Ol7QO0RZuvO1vdHdYi4ekYcBVwKnAY7bnSHoC2L27I8tPTfGtVCqtIGmC7dHdHkfbSJpse6SkGY2ZlaSJtrfr9tgqlV5B0nTgTbb/mV6/DLipF9pySdoZ2JFIPbzO9rVdHlKl0nNIGk6k+G4PHAI8aXuD7o5q8Ul9arft9jhKU3dQK5VKWxwn6WxgHH3dbi/v3pBa4QlJSwPTJJ1KtJt5aZfHVKn0GqcBEyVdRuw07gt8sbtDagfbPwN+1u1xVCq9iqRxxHN5EtE2Z5Ttv3Z3VK1xnaS9gMuXpH7sdQe1Uqm0gqQLgA2AWcxL8bXt93VvVItPcl7+C7A04Z63IvAt27/p6sAqlR5D0obADsRO47he6BctaRvgdOC1xD1kOPC47RW6OrBKpYeQ9DWit+vTwC3AeGCS7Se7OrAWkDSbCL7nEK1mmvaEPX0PqQFqpVJpBUkzeyEdbyCSscmatu/t9lgqlcrQQdJk5q9h/0/bx3Z1YJVKDyJpOeBg4JPAararIdkQpbr4ViqVtrg17YD0FJJ2JQwXrkmvN5N0ZXdHValUhgop22K47Tm2xxI1cpVKpSUkHSrph8Szeg/gXGDn7o6qPSTtJukr6WuXbo+nBLUGtVKptMVo4CBJ9xNpNk0ayibdHdZiczywFXAjgO1pktbu3nAqlcoQotawVyr5WQb4KjDF9rPdHkybSDqZMH+6MB0aI2m07c90cVjZqSm+lUqlFVKt5nzYfqD0WNpE0m22t5Y01fbm6diMHgi8K5VKZmoNe6VSWRxSP/nNbD+XXg8Hpvb6HKTuoFYqlVYY6oHoQrhT0ruA4ZLWAw4DJnZ5TJVKZZCTJpJftH0g8BTw+S4PqVKpDE1WAh5Of1+xmwMpRa1BrVQqlYXzcWAjIm35IuAx4PCujqhSqQx6bM8BVkkpvpVKpfJCOAmYKuk8SecDU4AvdXlM2akpvpVKpVKpVCoZkHQmsAVwJfB4c9z2V7s2qEqlMqSQtDpRhyrgNtt/7vKQslNTfCuVSmUhSBoJHAOsTcc9s9frPyqVSiv8KX0NA5bv8lgqlcoQQ9L3ib6uN9u+p9vjKUUNUCuVSmXhXAgcBcwEnuvyWCqVyhBA0vdtvxt4xPY3uj2eSqUyZBlLdEk4XdI6RCud8b1+X6kpvpVKpbIQJE2wPbrb46hUKkMHSXcRfRivBN5EpObNxfbDA7ytUqlU5iMZro0ieigfAjxpe4PujiovNUCtVCqVhSBpR2B/YBxhlASA7cu7NqhKpTKokXQY8BFgHeCP9A1QbXudrgysUqkMKSSNI3onTwJuBibY/mt3R5WfGqBWKpXKQpB0AbABMIt5Kb62/b7ujapSqQwFJH3b9ke6PY5KpTI0kfQ1YEtigfwWoh51ku0nuzqwzNQAtVKpVBaCpJm2X9ftcVQqlUqlUlkykbQccDDwSWA12y/u8pCyUk2SKpVKZeHcKmlD23d1eyCVSqVSqVSWHCQdCryB2EV9ADiXSPXtaeoOaqVSqSwESXcD6wL3Eyk2IlJ8a5uZSqVSqVQq2ZB0FJHWO8X2s90eTylqgFqpVCoLQdJaAx23/UDpsVQqlUqlUqn0OjVArVQqlUqlUqlUKpXKoGBYtwdQqVQqlUqlUqlUKpUK1AC1UqlUKpVKpVKpVCqDhBqgViqVSqVSqVQqlUplUFAD1EqlUqlUKpVKpVKpDAr+P1WygzJ88EpnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.bar(dados['feature_names'],rf.feature_importances_)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = pd.DataFrame({'label':dados['feature_names'], 'importance': rf.feature_importances_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>worst radius</td>\n",
       "      <td>0.135367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>worst perimeter</td>\n",
       "      <td>0.133119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>worst concave points</td>\n",
       "      <td>0.121512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>worst area</td>\n",
       "      <td>0.092459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>area error</td>\n",
       "      <td>0.062376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mean concavity</td>\n",
       "      <td>0.057589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mean radius</td>\n",
       "      <td>0.055221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mean concave points</td>\n",
       "      <td>0.054599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mean perimeter</td>\n",
       "      <td>0.052648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>worst concavity</td>\n",
       "      <td>0.041990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mean area</td>\n",
       "      <td>0.028376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean texture</td>\n",
       "      <td>0.017733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>perimeter error</td>\n",
       "      <td>0.016509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>worst compactness</td>\n",
       "      <td>0.015528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>worst texture</td>\n",
       "      <td>0.015236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   label  importance\n",
       "20          worst radius    0.135367\n",
       "22       worst perimeter    0.133119\n",
       "27  worst concave points    0.121512\n",
       "23            worst area    0.092459\n",
       "13            area error    0.062376\n",
       "6         mean concavity    0.057589\n",
       "0            mean radius    0.055221\n",
       "7    mean concave points    0.054599\n",
       "2         mean perimeter    0.052648\n",
       "26       worst concavity    0.041990\n",
       "3              mean area    0.028376\n",
       "1           mean texture    0.017733\n",
       "12       perimeter error    0.016509\n",
       "25     worst compactness    0.015528\n",
       "21         worst texture    0.015236"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.sort_values(by=['importance'], ascending=False)[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como podemos notar, o algoritmo genético e o modelo de random forest retornam praticamente o mesmo resultado na seleção das features mais significativas!"
   ]
  }
 ],
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