Mod17_tarefa02.ipynb

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    "<img src=\"https://raw.githubusercontent.com/rhatiro/Curso_EBAC-Profissao_Cientista_de_Dados/main/ebac-course-utils/media/logo/newebac_logo_black_half.png\" alt=\"ebac-logo\">\n",
    "\n",
    "---\n",
    "\n",
    "# **Profissão: Cientista de Dados**\n",
    "### **Módulo 17** | Exercício 2 - Árvores II\n",
    "\n",
    "Aluna [Lays Félix](https://www.linkedin.com/in/laysfelixbusiness)<br>\n",
    "Data: 31 de julho de 2024\n",
    "\n",
    "---"
   ]
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    "### 1. Carregar as bases\n",
    "\n",
    "Vamos carregar as bases lidas na tarefa passada. Se você salvou essas bases em arquivo texto, basta fazer a leitura com o comando ```pd.read_csv``` das seguintes bases:\n",
    "\n",
    "- X_train\n",
    "- Y_train\n",
    "- X_test\n",
    "- Y_test\n",
    "\n",
    "Não se esqueça de considerar a leitura dos índices dos arquivos no ```read_csv()```!"
   ]
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   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import ConfusionMatrixDisplay"
   ]
  },
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   "source": [
    "y_train = pd.read_csv('./output/y_train.csv')\n",
    "X_train = pd.read_csv('./output/X_train.csv')\n",
    "\n",
    "y_test = pd.read_csv('./output/y_test.csv')\n",
    "X_test = pd.read_csv('./output/X_test.csv')"
   ]
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>1 tBodyAcc-mean()-X</th>\n",
       "      <th>2 tBodyAcc-mean()-Y</th>\n",
       "      <th>3 tBodyAcc-mean()-Z</th>\n",
       "      <th>4 tBodyAcc-std()-X</th>\n",
       "      <th>5 tBodyAcc-std()-Y</th>\n",
       "      <th>6 tBodyAcc-std()-Z</th>\n",
       "      <th>7 tBodyAcc-mad()-X</th>\n",
       "      <th>8 tBodyAcc-mad()-Y</th>\n",
       "      <th>9 tBodyAcc-mad()-Z</th>\n",
       "      <th>10 tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>552 fBodyBodyGyroJerkMag-meanFreq()</th>\n",
       "      <th>553 fBodyBodyGyroJerkMag-skewness()</th>\n",
       "      <th>554 fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>555 angle(tBodyAccMean,gravity)</th>\n",
       "      <th>556 angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>557 angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>558 angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>559 angle(X,gravityMean)</th>\n",
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       "      <th>561 angle(Z,gravityMean)</th>\n",
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       "      <td>-0.983185</td>\n",
       "      <td>-0.923527</td>\n",
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       "      <td>-0.074323</td>\n",
       "      <td>-0.298676</td>\n",
       "      <td>-0.710304</td>\n",
       "      <td>-0.112754</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>-0.464761</td>\n",
       "      <td>-0.018446</td>\n",
       "      <td>-0.841247</td>\n",
       "      <td>0.179941</td>\n",
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       "      <th>1</th>\n",
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       "      <td>-0.123520</td>\n",
       "      <td>-0.998245</td>\n",
       "      <td>-0.975300</td>\n",
       "      <td>-0.960322</td>\n",
       "      <td>-0.998807</td>\n",
       "      <td>-0.974914</td>\n",
       "      <td>-0.957686</td>\n",
       "      <td>-0.943068</td>\n",
       "      <td>...</td>\n",
       "      <td>0.158075</td>\n",
       "      <td>-0.595051</td>\n",
       "      <td>-0.861499</td>\n",
       "      <td>0.053477</td>\n",
       "      <td>-0.007435</td>\n",
       "      <td>-0.732626</td>\n",
       "      <td>0.703511</td>\n",
       "      <td>-0.844788</td>\n",
       "      <td>0.180289</td>\n",
       "      <td>-0.054317</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.279653</td>\n",
       "      <td>-0.019467</td>\n",
       "      <td>-0.113462</td>\n",
       "      <td>-0.995380</td>\n",
       "      <td>-0.967187</td>\n",
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       "      <td>-0.963668</td>\n",
       "      <td>-0.977469</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>0.414503</td>\n",
       "      <td>-0.390748</td>\n",
       "      <td>-0.760104</td>\n",
       "      <td>-0.118559</td>\n",
       "      <td>0.177899</td>\n",
       "      <td>0.100699</td>\n",
       "      <td>0.808529</td>\n",
       "      <td>-0.848933</td>\n",
       "      <td>0.180637</td>\n",
       "      <td>-0.049118</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.279174</td>\n",
       "      <td>-0.026201</td>\n",
       "      <td>-0.123283</td>\n",
       "      <td>-0.996091</td>\n",
       "      <td>-0.983403</td>\n",
       "      <td>-0.990675</td>\n",
       "      <td>-0.997099</td>\n",
       "      <td>-0.982750</td>\n",
       "      <td>-0.989302</td>\n",
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       "      <td>-0.036788</td>\n",
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       "      <td>-0.016570</td>\n",
       "      <td>-0.115362</td>\n",
       "      <td>-0.998139</td>\n",
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       "      <td>-0.990482</td>\n",
       "      <td>-0.998321</td>\n",
       "      <td>-0.979672</td>\n",
       "      <td>-0.990441</td>\n",
       "      <td>-0.942469</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087753</td>\n",
       "      <td>-0.351471</td>\n",
       "      <td>-0.699205</td>\n",
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       "      <td>0.185151</td>\n",
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       "   1 tBodyAcc-mean()-X  2 tBodyAcc-mean()-Y  3 tBodyAcc-mean()-Z  \\\n",
       "0             0.288585            -0.020294            -0.132905   \n",
       "1             0.278419            -0.016411            -0.123520   \n",
       "2             0.279653            -0.019467            -0.113462   \n",
       "3             0.279174            -0.026201            -0.123283   \n",
       "4             0.276629            -0.016570            -0.115362   \n",
       "\n",
       "   4 tBodyAcc-std()-X  5 tBodyAcc-std()-Y  6 tBodyAcc-std()-Z  \\\n",
       "0           -0.995279           -0.983111           -0.913526   \n",
       "1           -0.998245           -0.975300           -0.960322   \n",
       "2           -0.995380           -0.967187           -0.978944   \n",
       "3           -0.996091           -0.983403           -0.990675   \n",
       "4           -0.998139           -0.980817           -0.990482   \n",
       "\n",
       "   7 tBodyAcc-mad()-X  8 tBodyAcc-mad()-Y  9 tBodyAcc-mad()-Z  \\\n",
       "0           -0.995112           -0.983185           -0.923527   \n",
       "1           -0.998807           -0.974914           -0.957686   \n",
       "2           -0.996520           -0.963668           -0.977469   \n",
       "3           -0.997099           -0.982750           -0.989302   \n",
       "4           -0.998321           -0.979672           -0.990441   \n",
       "\n",
       "   10 tBodyAcc-max()-X  ...  552 fBodyBodyGyroJerkMag-meanFreq()  \\\n",
       "0            -0.934724  ...                            -0.074323   \n",
       "1            -0.943068  ...                             0.158075   \n",
       "2            -0.938692  ...                             0.414503   \n",
       "3            -0.938692  ...                             0.404573   \n",
       "4            -0.942469  ...                             0.087753   \n",
       "\n",
       "   553 fBodyBodyGyroJerkMag-skewness()  554 fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "0                            -0.298676                            -0.710304   \n",
       "1                            -0.595051                            -0.861499   \n",
       "2                            -0.390748                            -0.760104   \n",
       "3                            -0.117290                            -0.482845   \n",
       "4                            -0.351471                            -0.699205   \n",
       "\n",
       "   555 angle(tBodyAccMean,gravity)  556 angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "0                        -0.112754                                  0.030400   \n",
       "1                         0.053477                                 -0.007435   \n",
       "2                        -0.118559                                  0.177899   \n",
       "3                        -0.036788                                 -0.012892   \n",
       "4                         0.123320                                  0.122542   \n",
       "\n",
       "   557 angle(tBodyGyroMean,gravityMean)  \\\n",
       "0                             -0.464761   \n",
       "1                             -0.732626   \n",
       "2                              0.100699   \n",
       "3                              0.640011   \n",
       "4                              0.693578   \n",
       "\n",
       "   558 angle(tBodyGyroJerkMean,gravityMean)  559 angle(X,gravityMean)  \\\n",
       "0                                 -0.018446                 -0.841247   \n",
       "1                                  0.703511                 -0.844788   \n",
       "2                                  0.808529                 -0.848933   \n",
       "3                                 -0.485366                 -0.848649   \n",
       "4                                 -0.615971                 -0.847865   \n",
       "\n",
       "   560 angle(Y,gravityMean)  561 angle(Z,gravityMean)  \n",
       "0                  0.179941                 -0.058627  \n",
       "1                  0.180289                 -0.054317  \n",
       "2                  0.180637                 -0.049118  \n",
       "3                  0.181935                 -0.047663  \n",
       "4                  0.185151                 -0.043892  \n",
       "\n",
       "[5 rows x 561 columns]"
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       "      <td>-0.920091</td>\n",
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       "      <td>-0.330370</td>\n",
       "      <td>-0.705974</td>\n",
       "      <td>0.006462</td>\n",
       "      <td>0.162920</td>\n",
       "      <td>-0.825886</td>\n",
       "      <td>0.271151</td>\n",
       "      <td>-0.720009</td>\n",
       "      <td>0.276801</td>\n",
       "      <td>-0.057978</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.286027</td>\n",
       "      <td>-0.013163</td>\n",
       "      <td>-0.119083</td>\n",
       "      <td>-0.975415</td>\n",
       "      <td>-0.967458</td>\n",
       "      <td>-0.944958</td>\n",
       "      <td>-0.986799</td>\n",
       "      <td>-0.968401</td>\n",
       "      <td>-0.945823</td>\n",
       "      <td>-0.894088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.401189</td>\n",
       "      <td>-0.121845</td>\n",
       "      <td>-0.594944</td>\n",
       "      <td>-0.083495</td>\n",
       "      <td>0.017500</td>\n",
       "      <td>-0.434375</td>\n",
       "      <td>0.920593</td>\n",
       "      <td>-0.698091</td>\n",
       "      <td>0.281343</td>\n",
       "      <td>-0.083898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.275485</td>\n",
       "      <td>-0.026050</td>\n",
       "      <td>-0.118152</td>\n",
       "      <td>-0.993819</td>\n",
       "      <td>-0.969926</td>\n",
       "      <td>-0.962748</td>\n",
       "      <td>-0.994403</td>\n",
       "      <td>-0.970735</td>\n",
       "      <td>-0.963483</td>\n",
       "      <td>-0.939260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062891</td>\n",
       "      <td>-0.190422</td>\n",
       "      <td>-0.640736</td>\n",
       "      <td>-0.034956</td>\n",
       "      <td>0.202302</td>\n",
       "      <td>0.064103</td>\n",
       "      <td>0.145068</td>\n",
       "      <td>-0.702771</td>\n",
       "      <td>0.280083</td>\n",
       "      <td>-0.079346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.270298</td>\n",
       "      <td>-0.032614</td>\n",
       "      <td>-0.117520</td>\n",
       "      <td>-0.994743</td>\n",
       "      <td>-0.973268</td>\n",
       "      <td>-0.967091</td>\n",
       "      <td>-0.995274</td>\n",
       "      <td>-0.974471</td>\n",
       "      <td>-0.968897</td>\n",
       "      <td>-0.938610</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116695</td>\n",
       "      <td>-0.344418</td>\n",
       "      <td>-0.736124</td>\n",
       "      <td>-0.017067</td>\n",
       "      <td>0.154438</td>\n",
       "      <td>0.340134</td>\n",
       "      <td>0.296407</td>\n",
       "      <td>-0.698954</td>\n",
       "      <td>0.284114</td>\n",
       "      <td>-0.077108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.274833</td>\n",
       "      <td>-0.027848</td>\n",
       "      <td>-0.129527</td>\n",
       "      <td>-0.993852</td>\n",
       "      <td>-0.967445</td>\n",
       "      <td>-0.978295</td>\n",
       "      <td>-0.994111</td>\n",
       "      <td>-0.965953</td>\n",
       "      <td>-0.977346</td>\n",
       "      <td>-0.938610</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.121711</td>\n",
       "      <td>-0.534685</td>\n",
       "      <td>-0.846595</td>\n",
       "      <td>-0.002223</td>\n",
       "      <td>-0.040046</td>\n",
       "      <td>0.736715</td>\n",
       "      <td>-0.118545</td>\n",
       "      <td>-0.692245</td>\n",
       "      <td>0.290722</td>\n",
       "      <td>-0.073857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 561 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1 tBodyAcc-mean()-X  2 tBodyAcc-mean()-Y  3 tBodyAcc-mean()-Z  \\\n",
       "0             0.257178            -0.023285            -0.014654   \n",
       "1             0.286027            -0.013163            -0.119083   \n",
       "2             0.275485            -0.026050            -0.118152   \n",
       "3             0.270298            -0.032614            -0.117520   \n",
       "4             0.274833            -0.027848            -0.129527   \n",
       "\n",
       "   4 tBodyAcc-std()-X  5 tBodyAcc-std()-Y  6 tBodyAcc-std()-Z  \\\n",
       "0           -0.938404           -0.920091           -0.667683   \n",
       "1           -0.975415           -0.967458           -0.944958   \n",
       "2           -0.993819           -0.969926           -0.962748   \n",
       "3           -0.994743           -0.973268           -0.967091   \n",
       "4           -0.993852           -0.967445           -0.978295   \n",
       "\n",
       "   7 tBodyAcc-mad()-X  8 tBodyAcc-mad()-Y  9 tBodyAcc-mad()-Z  \\\n",
       "0           -0.952501           -0.925249           -0.674302   \n",
       "1           -0.986799           -0.968401           -0.945823   \n",
       "2           -0.994403           -0.970735           -0.963483   \n",
       "3           -0.995274           -0.974471           -0.968897   \n",
       "4           -0.994111           -0.965953           -0.977346   \n",
       "\n",
       "   10 tBodyAcc-max()-X  ...  552 fBodyBodyGyroJerkMag-meanFreq()  \\\n",
       "0            -0.894088  ...                             0.071645   \n",
       "1            -0.894088  ...                            -0.401189   \n",
       "2            -0.939260  ...                             0.062891   \n",
       "3            -0.938610  ...                             0.116695   \n",
       "4            -0.938610  ...                            -0.121711   \n",
       "\n",
       "   553 fBodyBodyGyroJerkMag-skewness()  554 fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "0                            -0.330370                            -0.705974   \n",
       "1                            -0.121845                            -0.594944   \n",
       "2                            -0.190422                            -0.640736   \n",
       "3                            -0.344418                            -0.736124   \n",
       "4                            -0.534685                            -0.846595   \n",
       "\n",
       "   555 angle(tBodyAccMean,gravity)  556 angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "0                         0.006462                                  0.162920   \n",
       "1                        -0.083495                                  0.017500   \n",
       "2                        -0.034956                                  0.202302   \n",
       "3                        -0.017067                                  0.154438   \n",
       "4                        -0.002223                                 -0.040046   \n",
       "\n",
       "   557 angle(tBodyGyroMean,gravityMean)  \\\n",
       "0                             -0.825886   \n",
       "1                             -0.434375   \n",
       "2                              0.064103   \n",
       "3                              0.340134   \n",
       "4                              0.736715   \n",
       "\n",
       "   558 angle(tBodyGyroJerkMean,gravityMean)  559 angle(X,gravityMean)  \\\n",
       "0                                  0.271151                 -0.720009   \n",
       "1                                  0.920593                 -0.698091   \n",
       "2                                  0.145068                 -0.702771   \n",
       "3                                  0.296407                 -0.698954   \n",
       "4                                 -0.118545                 -0.692245   \n",
       "\n",
       "   560 angle(Y,gravityMean)  561 angle(Z,gravityMean)  \n",
       "0                  0.276801                 -0.057978  \n",
       "1                  0.281343                 -0.083898  \n",
       "2                  0.280083                 -0.079346  \n",
       "3                  0.284114                 -0.077108  \n",
       "4                  0.290722                 -0.073857  \n",
       "\n",
       "[5 rows x 561 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8eaakz05dLte"
   },
   "source": [
    "### 2. Divisão da base em Treino, Validação e Teste\n",
    "\n",
    "A base já se encontra dividida em Treino e Validação. O que vamos fazer então é extrair uma base de Validação da base de Treino.\n",
    "\n",
    "Extraia 25% da base de treino como base de validação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "hZn6WMUAdLte"
   },
   "outputs": [],
   "source": [
    "X = X_test\n",
    "y = y_test\n",
    "\n",
    "X_, X_test, y_, y_test = train_test_split(X, y, test_size=.2, random_state=42)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_, y_, train_size=.25, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gl4BaRHbdLte"
   },
   "source": [
    "### 3. Melhores 3 variáveis\n",
    "\n",
    "Rode uma árvore com profundidade máxima igual a 4 para prever a atividade humana com todas as variáveis.\n",
    "Observe a importância das variáveis e considere as 3 variáveis com maior importância para os próximos passos.\n",
    "Dica: utilize o atributo ```clf.feature_importances_``` da árvore treinada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HSQCDaeYdLte",
    "outputId": "42feca82-7c5b-4bdc-e64b-a526bab8cb1d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importância das variáveis: [0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.12373681\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.13200649 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.04086362 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.25042424 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.01598294 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.01403531 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.14248508 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.28046551 0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=4)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "importances = clf.feature_importances_\n",
    "print('Importância das variáveis:', importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YadQTjQ9dLtf",
    "outputId": "88762919-78ff-4faa-b0e2-d4ac05132f2f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "559 angle(X,gravityMean)     0.280466\n",
       "345 fBodyAccJerk-mean()-X    0.250424\n",
       "505 fBodyAccMag-mad()        0.142485\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importancia_das_variaveis = pd.Series(clf.feature_importances_, index=X_train.columns)\n",
    "importancia_das_variaveis.sort_values(ascending=False, inplace=True)\n",
    "importancia_das_variaveis.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "buuIp2rhdLtf"
   },
   "source": [
    "### 4. Construa uma árvore com as 3 melhores variáveis\n",
    "\n",
    "Utilizando as três variáveis encontradas acima, construa uma árvore de decisão. Encontre o melhor ```ccp_alpha``` utilizando a base de validação, conforme a estrutura que vimos em aula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dmFScDTIdLtf",
    "outputId": "f9535e86-ff6a-4860-8481-aa122d060d0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 745 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "X_train_cut = X_train[['559 angle(X,gravityMean)', '345 fBodyAccJerk-mean()-X', '505 fBodyAccMag-mad()']]\n",
    "X_valid_cut = X_valid[['559 angle(X,gravityMean)', '345 fBodyAccJerk-mean()-X', '505 fBodyAccMag-mad()']]\n",
    "X_test_cut = X_test[['559 angle(X,gravityMean)', '345 fBodyAccJerk-mean()-X', '505 fBodyAccMag-mad()']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wzoQbcTgoGr7",
    "outputId": "b34c7340-a439-464c-b9aa-8e610c2df382"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.00037707, 0.00037707, 0.00042421, 0.00042421,\n",
       "       0.00042421, 0.00045249, 0.00045249, 0.00045249, 0.00045249,\n",
       "       0.0004658 , 0.00048481, 0.00048481, 0.00049491, 0.00050277,\n",
       "       0.00050277, 0.00050277, 0.00050938, 0.00051419, 0.00051848,\n",
       "       0.00051848, 0.00051848, 0.00051935, 0.0005221 , 0.00052521,\n",
       "       0.00052521, 0.0005279 , 0.00053234, 0.00053584, 0.00054676,\n",
       "       0.00054847, 0.00054898, 0.00055111, 0.00055147, 0.00055276,\n",
       "       0.00055776, 0.00056561, 0.00056561, 0.00056561, 0.00056561,\n",
       "       0.00057504, 0.00058581, 0.00061086, 0.00062689, 0.00062958,\n",
       "       0.00065045, 0.00065359, 0.000675  , 0.00067873, 0.00070297,\n",
       "       0.00070701, 0.00073529, 0.00073529, 0.00075415, 0.00075415,\n",
       "       0.00077105, 0.00081118, 0.00081312, 0.00084842, 0.00084842,\n",
       "       0.00084842, 0.00085543, 0.00086032, 0.00086727, 0.00087017,\n",
       "       0.00088673, 0.00089057, 0.00089503, 0.00092   , 0.00094268,\n",
       "       0.00094268, 0.00095261, 0.00096962, 0.00098039, 0.00098982,\n",
       "       0.00100553, 0.00101189, 0.0010181 , 0.0010181 , 0.00103683,\n",
       "       0.00107168, 0.00110311, 0.00111216, 0.0011226 , 0.0011396 ,\n",
       "       0.00114652, 0.00120435, 0.00122414, 0.00123556, 0.00127262,\n",
       "       0.00134998, 0.00136151, 0.00138832, 0.00151112, 0.00153382,\n",
       "       0.00161603, 0.00175616, 0.00188338, 0.00196617, 0.00198446,\n",
       "       0.00212777, 0.00217372, 0.00234839, 0.00251916, 0.00260585,\n",
       "       0.00267656, 0.00277304, 0.00284559, 0.00296605, 0.0030124 ,\n",
       "       0.00366373, 0.0051119 , 0.00534115, 0.00593861, 0.00746116,\n",
       "       0.00899905, 0.01039722, 0.019239  , 0.02230901, 0.03405027,\n",
       "       0.03852225, 0.0906769 , 0.16834165, 0.17772592])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caminho = DecisionTreeClassifier(random_state=2360873).cost_complexity_pruning_path(X_valid_cut, y_valid)\n",
    "ccp_alphas, impurities = caminho.ccp_alphas, caminho.impurities\n",
    "ccp_alphas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jYtSfSF-oX3b",
    "outputId": "21deed78-9be4-446a-a3c1-10cc818cbe6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1.3 s\n",
      "Wall time: 3.87 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "clfs = []\n",
    "for ccp_alpha in ccp_alphas:\n",
    "    clf = DecisionTreeClassifier(random_state=2360873, ccp_alpha=ccp_alpha).fit(X_valid_cut, y_valid)\n",
    "    clfs.append(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "51DCPgnhoXt7",
    "outputId": "310144be-d3d9-4e3e-8517-bddda0de1b3c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 766 ms\n",
      "Wall time: 2.13 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_scores = [clf.score(X_train_cut, y_train) for clf in clfs]\n",
    "valid_scores = [clf.score(X_valid_cut, y_valid) for clf in clfs]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel(\"Alpha\")\n",
    "ax.set_ylabel(\"Acurácia\")\n",
    "ax.set_title(\"Acurácia x Alpha do conjunto de dados de Treino e Validação\")\n",
    "ax.plot(ccp_alphas, train_scores, marker='o', label=\"Validação\",\n",
    "        drawstyle=\"steps-post\")\n",
    "ax.plot(ccp_alphas, valid_scores, marker='o', label=\"Teste\",\n",
    "        drawstyle=\"steps-post\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 92
    },
    "id": "h7EUc92ioXnj",
    "outputId": "3eca4b86-3e63-40b5-967e-dc60891fe97c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia da melhor árvore na base de validação: 100.00\n",
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=2360873)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "ind_melhor_arvore = len(valid_scores) - valid_scores[::-1].index(max(valid_scores)) - 1\n",
    "melhor_arvore = clfs[ind_melhor_arvore]\n",
    "\n",
    "print(f'Acurácia da melhor árvore na base de validação: {valid_scores[ind_melhor_arvore]*100:.2f}')\n",
    "\n",
    "melhor_arvore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2P2Ottg6dLtf"
   },
   "source": [
    "### 5. Avaliação do modelo\n",
    "\n",
    "Avalie a árvore encontrada no item anterior na base de testes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "Jx4J59YboXbT",
    "outputId": "9f12486c-4ebd-484f-a499-441ea0c070bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia: 82.03\n"
     ]
    },
    {
     "data": {
      "image/png": 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2cSs3R+Y6F5d64fJ/vNBougXt15rRYJIFF5d7IXu1/cclsLsVHX802ZYW/7JIFLHrTHz9IOLuuYJ3E9tj/Iie2Lc7FHMW7UZIaJnUoblMr0fzMHbWRaxeEIZx/Zvh8G4/zP48A6H1yqUOzaWUmLfScrZCcHipiblz5+Kjjz7CokWLcOzYMbzzzjv45z//iYULF9q2eeeddzBv3jwsWrQIe/fuRUREBPr164fCwkKn5i5psU9OTkbnzp2h1+sRFhaGIUOG4Pjx41KGVEna7nCs+KQVdm6LqvL5nzY3wOrlLbA/PdTNkblO4W8CgnqLCOopQlcPCOknok5XEcVH7D/oXt6Ad90bizpQooBdxFtrQff7s7F0UQscORCCS+f9sOqTZrh80RcPD82UOjyXGfrCFWxaHYyNq0KQdUqHj2bWQ+5FDQY+dVXq0FxKiXkrMWd3+vXXXzF48GA88sgjaNSoER577DH0798faWlpACq6+vnz52PGjBkYOnQo2rRpg+XLl6OkpASrVq1yaiySFvvU1FSMHz8eu3btwpYtW2A2m9G/f38UFxdLGZbi6eNEFOwRUHq24nHxcaBwv4A691nttjOkCUjrrcaBQWqcmaWCSWZ/H1QqESq1iHKj/eEJo9ELrdpfkygq11JrrIhtV4L0VL3d+vRUPVrFy/f3Uol5KzHn61fQc2QBAIPBYLcYjcYq369Hjx748ccfceLECQDAb7/9hu3bt+Phhx8GAGRkZCA7Oxv9+/e37aPVatGrVy/s3LnTqblLOht/48aNdo+XLl2KsLAwpKeno2fPnhJFRVHPWmEpAn4booagAkQLED3BiroDbkxFrdPdipB+gDZShPGCgKwPVTj6vBpt15jh5S1h8E5UWqLGsYN1MOzZk8g664/8a1r06n8BzVvn42KWn9ThuURAsAUqNZB/xf5PQ36uGkFhZomicj0l5q3EnGtz3P3W/QEgOjrabv3MmTORmJhYaftp06ahoKAALVq0gEqlgsViwZw5c/DXv/4VAJCdnQ0ACA8Pt9svPDwcmZnOHT28q069KygoAAAEBwdX+bzRaLT7BmUwGNwSl9Jc3SjgyvdeaJpsgW9TEcW/C8j8pwreoSJCH60o+HUfulH4fWNF+LU2Y/9DauRvExDc14PPT7nFu4kdkPDGQaz8/kdYzAJOHQ9A6qYoNGkh78+eeMuPUBAAyOfHeltKzFuJOTsqKysLAQEBtsdarbbK7b744gt89tlnWLVqFVq3bo0DBw4gISEBUVFRGDVqlG07QbA/RCqKYqV1jrprir0oipg8eTJ69OiBNm3aVLlNcnIyZs2a5ebIlOfc+ypEPXujk/eNFWG8ZMWFT1UIfbTqb/zeoYA2Cig9J6+/FNkX/PDai12h1Znh62dG3lUdps3eh8sXfaQOzSUM11SwmIGgUPufc2BdM/Jy75o/F06nxLyVmLMVDl4b/48JegEBAXbF/nZeffVVvPbaaxg2bBgAoG3btsjMzERycjJGjRqFiIgIABUdfmRkpG2/nJycSt2+o+6a2fgvvfQSDh48iNWrV992m+nTp6OgoMC2ZGVluTFC5bCWodInQ1ABsFa1dQVTPmDMBrxD5VPob2YsUyPvqg7+ehM63puLXdsipA7JJcwmL5w86IuOPe1nAnfsWYijafI8dAEoM28l5iw6OBNfrOFs/JKSEnh52f8xValUtlPvYmJiEBERgS1bttieLy8vR2pqKrp16+Z4wje5K76+TZgwAevXr8e2bdtQv379226n1WpvO1ziKjofM6Lq3TjvNDyyBI2b5qPQ4I3cHF/468sRFl6C4LoVp2LVb1Cxbd41HfKu6dwaq7PU6SXi4hIvaCNE+DQRUfK7gEsrvRA6uOIDaikBzqd4IbivCE1dEcaLArIWekFTBwh+QF7FvmOXXAiCiPOZ/oiMLsboCb/jQqY/tnx3+8+pp/tmcV28uiALJw764FiaHx4eeRVh9Uz4fkWI1KG5lBLzVlrO7r7r3aBBgzBnzhw0aNAArVu3xv79+zFv3jw8++yzACqG7xMSEpCUlITY2FjExsYiKSkJvr6+GD58eK3jrIqkxV4URUyYMAFr167F1q1bERMTI2U4VYptnoe5C3bYHr8w4TAAYMuGaLyf3An3dr+Eya/vtz3/WmLFKRWfL22Oz5e2dG+wThLzmgVZ//JCRpIKpmsVQ/Thj1lRb0xFsRe8gJKTAnK/84KlENCEAgGdRcS+Y4ZKZg2Br78JT487jrphZSg0aLDj5wisSGkOi+WuGRRzutT1QdAHWTBi0mUEh5mReVyHN0bGIOeCTGZe3oYS81Zizu60cOFCvPnmmxg3bhxycnIQFRWFMWPG4K233rJtM3XqVJSWlmLcuHHIy8tDly5dsHnzZuj1+j955ZoTRPHW6RnuM27cOKxatQrffvstmjdvblsfGBgIH587HxM1GAwIDAxEn8YToVa5t+OXWuevT0gdgtulDbz7vgy6gznrvNQhELmMWTRhK75FQUFBtY6D18b1WvF/W56Bxq/2X2RMxeVY22+pS2N1FUk7+5SUFABA79697dYvXboUTz/9tPsDIiIi2XL3MP7dRPJhfCIiInKtu2KCHhERkavV5vr2t+7vqVjsiYhIEZQ8jC/fKcVEREQEgJ09EREphJI7exZ7IiJSBCUXew7jExERyRw7eyIiUgQld/Ys9kREpAgiHDt9zpOvDMNiT0REiqDkzp7H7ImIiGSOnT0RESmCkjt7FnsiIlIEJRd7DuMTERHJHDt7IiJSBCV39iz2RESkCKIoQHSgYDuyr9Q4jE9ERCRz7OyJiEgReD97IiIimVPyMXsO4xMREckcO3siIlIEJU/QY7EnIiJFUPIwPos9EREpgpI7ex6zJyIikjl5dPZ5BYDgLXUUbrW3T4TUIbid4TNl/Yyv83tI6giI5EF0cBjfkzt7eRR7IiKiOxABiKJj+3sqDuMTERHJHDt7IiJSBCsECLyCHhERkXxxNj4RERHJFjt7IiJSBKsoQOBFdYiIiORLFB2cje/B0/E5jE9ERCRz7OyJiEgRlDxBj8WeiIgUgcWeiIhI5pQ8QY/H7ImIiGSOnT0RESmCkmfjs9gTEZEiVBR7R47ZOzEYN+MwPhERkcyxsyciIkXgbHwiIiKZE+HYPek9eBSfw/hERERyx86eiIgUgcP4REREcqfgcXwWeyIiUgYHO3t4cGfPY/ZEREQyx86eiIgUgVfQIyIikjklT9DjMD4REZHMsbOvoRHjMjBifKbdumtXNBjZq7tEEblHSJgRzyScQnyPq/DWWnEh0xcfzGyBU8cCpA7NKXyeOgevHHOl9aaBASgfGwLN8mtQ7y2BcMkM0c8LljgfmJ4Nhhgiz1+hgaOu4PEXcxEcZkLmCR0+eisKh/f4Sx2Wyykxb0XlLAqOTbLz4M5enn+pXOzsSV/MeK697bHF4rkfgOrw15vw7vJ0HNxbB2+N64D8axpERpeiqFA+H5/SBfUgWG8ckBPOlsPn9WyY7/MDjCJUp8pRPjwI1hhvCEVWeH98FdrEbJQtrC9h1K7R69E8jJ11EYter4cje/zwyN+uYvbnGXi+d3PkXvCWOjyXUWLeSstZycfsJR3GT0lJQbt27RAQEICAgAB07doVGzZskDKkarFYBORd0doWQ578filu9tizmci9rMX7b7XCicMByLnog992ByP7vK/UoTlPHRXEYLVtUe8pgTVSDWs7HeDnhbLkSFh6+kOM9oa1pQ7lL4ZAdbIcQhWjAZ5u6AtXsGl1MDauCkHWKR0+mlkPuRc1GPjUValDcykl5q3EnJVK0mJfv359vP3220hLS0NaWhoeeOABDB48GEeOHJEyrDuq16AUK3/eiX9v2oVp/zyCiPqlUofkUvf2voKTRwIw/d1DWLX1Fyz8Yg8e/MsFqcNyHZMI9U9FMD+oB4TbjNoUWytGBP3kNe1FrbEitl0J0lP1duvTU/VoFV8sUVSup8S8lZiz7aI6jiweStJx2EGDBtk9njNnDlJSUrBr1y60bt260vZGoxFGo9H22GAwuDzGWx0/GID3Xm+JC2d9UCekHMPGZOLdz/fhxUfvQWGBxu3xuENE/TI88sQFrF0ZjS8+aYTmbQwYO+0kTOVe+Om7SKnDczrVr8VAkRXmfvqqNyi3wnvpNVh6+wMyK/YBwRao1ED+Ffs/Dfm5agSFyW8U4zol5q3EnJU8G79axX7BggXVfsGJEyfWKhCLxYL//Oc/KC4uRteuXavcJjk5GbNmzarV6ztL2vaQGw9OAsd+C8SnG3eh75BsrF0eLV1gLiR4iTh5RI/lC5oAAM78rkeDJsV45IkLsiz26o2FsHT2rXrynVmENjkHsALGl+q6Pzg3ufXYpCDAo7ua6lJi3krMWYmqVezff//9ar2YIAg1LvaHDh1C165dUVZWBn9/f6xduxatWrWqctvp06dj8uTJtscGgwHR0dIWWGOpCpkn/BHVQL5D+Xm53sg642e3LivDF9375kgUkesIl01QHSiF8c3wyk+aRWiTLkPINqNsbqTsunoAMFxTwWIGgkLtO7vAumbk5cpnQuatlJi3EnMGoNgvMtX6iWZkZLgsgObNm+PAgQPIz8/H119/jVGjRiE1NbXKgq/VaqHVal0WS22oNVZENy7G4X2BUofiMkcP1EG9RiV26+o1LEXOJZ1EEbmOenMhxEAVLPfcMvnwj0LvdcGE0rlRQIBKmgBdzGzywsmDvujYsxA7N974THfsWYhfN8n3M67EvJWYs5KH8WvdmpSXl+P48eMwmx07tuPt7Y2mTZsiPj4eycnJaN++PT744AOHXtOVRk85hTbx+QivV4rmbQ2YMf8IfP0t+HFdhNShuczaldFo0daAJ547i8joEvR+OBsDHruA/66R2WlnVhHqLUUw9/MHVDf9UltEaGdfhtcJI4zTwiBYRQjXzBCumQGT/NqEbxbXxUPDr6H/sKuIblqGMYkXEFbPhO9XhNx5Zw+mxLwVl7MEE/QuXLiAkSNHIiQkBL6+vujQoQPS09NvhCSKSExMRFRUFHx8fNC7d2+XTFKv8VhNSUkJJkyYgOXLlwMATpw4gcaNG2PixImIiorCa6+95lBAoijaTcK729QNN2LaP48iIMiEgmsaHD8YgEnDO8qyy73u5JEAzJ7UFk+/fBrDx5xF9gUdPn4nFlt/kNcXHNX+UnjlmGHubz8xT8g1Q72rYmTDZ5z9WQilcyNhbe/jthjdIXV9EPRBFoyYdBnBYWZkHtfhjZExyJHhedc3U2LeSszZnfLy8tC9e3fcf//92LBhA8LCwnD69GnUqVPHts0777yDefPmYdmyZWjWrBlmz56Nfv364fjx49DrbzNJuBYEUazZZQJefvll7NixA/Pnz8dDDz2EgwcPonHjxli/fj1mzpyJ/fv3V/u1Xn/9dQwYMADR0dEoLCzEmjVr8Pbbb2Pjxo3o16/fHfc3GAwIDAxEn6BRUAsK+3Cq5He8+E4Mn8lzaPFO/B46I3UIRC5jFk3Yim9RUFCAgADXXJHzeq2I/igRXj61b8yspWXIGptY7Vhfe+017NixA7/88kuVz4uiiKioKCQkJGDatGkAKs46Cw8Px9y5czFmzJhax3qrGleMdevWYdGiRejRoweEm85BbtWqFU6fPl2j17p8+TL+9re/oXnz5ujTpw92795d7UJPRERUI04axjcYDHbL7Uaj169fj/j4eDz++OMICwtDXFwclixZYns+IyMD2dnZ6N+/v22dVqtFr169sHPnTqemXuNh/NzcXISFhVVaX1xcbFf8q+PTTz+t6dsTERFJ6tazwGbOnInExMRK2505cwYpKSmYPHkyXn/9dezZswcTJ06EVqvFU089hezsbABAeLj92T/h4eHIzMys9HqOqHGx79y5M77//ntMmDABAGwFfsmSJbc9P56IiEhyjl4F7499s7Ky7Ibxb3eWmNVqRXx8PJKSkgAAcXFxOHLkCFJSUvDUU0/Ztru1URZFscbN853UuNgnJyfjoYcewtGjR2E2m/HBBx/gyJEj+PXXX5GamurU4IiIiJzGSXe9u34/lzuJjIysdBp5y5Yt8fXXXwMAIiIqJjlnZ2cjMvLGBcpycnIqdfuOqvEx+27dumHHjh0oKSlBkyZNsHnzZoSHh+PXX39Fp06dnBocERGRp+revTuOHz9ut+7EiRNo2LAhACAmJgYRERHYsmWL7fny8nKkpqaiW7duTo2lVpdJatu2re3UOyIiIk/g7lvcTpo0Cd26dUNSUhKeeOIJ7NmzB4sXL8bixYsBVAzfJyQkICkpCbGxsYiNjUVSUhJ8fX0xfPjw2gdahVoVe4vFgrVr1+LYsWMQBAEtW7bE4MGDoVbL+BKLRETk2Zx0zL66OnfujLVr12L69On4+9//jpiYGMyfPx8jRoywbTN16lSUlpZi3LhxyMvLQ5cuXbB582annmMP1KLYHz58GIMHD0Z2djaaN28OoGJYIjQ0FOvXr0fbtm2dGiAREZGnGjhwIAYOHHjb5wVBQGJiYpWz+Z2pxsfsn3vuObRu3Rrnz5/Hvn37sG/fPmRlZaFdu3Z44YUXXBEjERGR465P0HNk8VA17ux/++03pKWlISgoyLYuKCgIc+bMQefOnZ0aHBERkbMIYsXiyP6eqsadffPmzXH58uVK63NyctC0aVOnBEVEROR0EtwI525RrWJ/82UBk5KSMHHiRHz11Vc4f/48zp8/j6+++goJCQmYO3euq+MlIiKiGqrWMH6dOnXsruYjiiKeeOIJ27rr99IZNGgQLBaLC8IkIiJykJMuquOJqlXsf/75Z1fHQURE5FpuPvXublKtYt+rVy9Xx0FEREQuUuur4JSUlODcuXMoLy+3W9+uXTuHgyIiInI6dvbVl5ubi2eeeQYbNmyo8nkesycioruSgot9jU+9S0hIQF5eHnbt2gUfHx9s3LgRy5cvR2xsLNavX++KGImIiMgBNe7sf/rpJ3z77bfo3LkzvLy80LBhQ/Tr1w8BAQFITk7GI4884oo4iYiIHKPg2fg17uyLi4sRFhYGAAgODkZubi6Aijvh7du3z7nREREROcn1K+g5sniqWl1B7/r9eTt06ICPP/4YFy5cwEcffYTIyEinB0hERESOqfEwfkJCAi5dugQAmDlzJh588EF8/vnn8Pb2xrJly5wdHxERkXMoeIJejYv9zffhjYuLw9mzZ/H777+jQYMGqFu3rlODIyIiIsfV+jz763x9fdGxY0dnxEJEROQyAhy8653TInG/ahX7yZMnV/sF582bV+tgiIiIyPmqVez3799frRe7+WY57mTJy4cgaCR5b3Ifv4euSh2CJMq3NJQ6BEmU/Vt5E34DVu+SOgR5U/Cpd7wRDhERKYOCJ+jV+NQ7IiIi8iwOT9AjIiLyCAru7FnsiYhIERy9Cp6irqBHREREnoWdPRERKYOCh/Fr1dmvXLkS3bt3R1RUFDIzMwEA8+fPx7fffuvU4IiIiJxGdMLioWpc7FNSUjB58mQ8/PDDyM/Ph8ViAQDUqVMH8+fPd3Z8RERE5KAaF/uFCxdiyZIlmDFjBlQqlW19fHw8Dh065NTgiIiInEXJt7it8TH7jIwMxMXFVVqv1WpRXFzslKCIiIicTsFX0KtxZx8TE4MDBw5UWr9hwwa0atXKGTERERE5n4KP2de4s3/11Vcxfvx4lJWVQRRF7NmzB6tXr0ZycjI++eQTV8RIREREDqhxsX/mmWdgNpsxdepUlJSUYPjw4ahXrx4++OADDBs2zBUxEhEROUzJF9Wp1Xn2zz//PJ5//nlcuXIFVqsVYWFhzo6LiIjIuRR8nr1DF9WpW7eus+IgIiIiF6lxsY+JifnT+9afOXPGoYCIiIhcwtHT55TU2SckJNg9NplM2L9/PzZu3IhXX33VWXERERE5F4fxq+/ll1+ucv2//vUvpKWlORwQEREROZfT7no3YMAAfP311856OSIiIufiefaO++qrrxAcHOyslyMiInIqnnpXA3FxcXYT9ERRRHZ2NnJzc/Hhhx86NTgiIiJyXI2L/ZAhQ+wee3l5ITQ0FL1790aLFi2cFRcRERE5SY2KvdlsRqNGjfDggw8iIiLCVTERERE5n4Jn49dogp5arcaLL74Io9HoqniIiIhcQsm3uK3xbPwuXbpg//79roiFiIiIXKDGx+zHjRuHV155BefPn0enTp3g5+dn93y7du2cFtzdauCoK3j8xVwEh5mQeUKHj96KwuE9/lKH5XLMW355a0aeh3DZUmm9ZZA/LBNDoFqRD6+txUCuBVALEGO9YXmmDsSWWgmidZ7/63oEQ7sdRWRwIQDgTHYQ/r2lE3b93uCPLUSM7p+OwfceQ4CvEUcyw/DuNz2QcVl+ZxzJ+fNdJQ/uzh1R7c7+2WefhcFgwJNPPomMjAxMnDgR3bt3R4cOHRAXF2f7t7aSk5MhCEKlK/TdbXo9moexsy5i9YIwjOvfDId3+2H25xkIrVcudWguxbzlmbdpUSTKv6hvW0xzK25qZe1V8SVerK+B+aVgmBZHwvx+OMRwNdSvXQbyK39B8CS5BX748PsueOb9oXjm/aFIP1UP7zyzCTHh1wAAI+//DX/tdRDvre2OZ+cPxdVCX3ww5nv4auXxc79O7p/vShR8nn21i/3y5ctRVlaGjIyMSsuZM2ds/9bG3r17sXjxYo8YFRj6whVsWh2MjatCkHVKh49m1kPuRQ0GPnVV6tBcinnLNO86KiD4xuK1qxRilBpiu4rO3fqAH8SOPkCkBmIjb1jGBkEoESGc8exisP1oI/z6ewNkXamDrCt18PGGe1BarkGbhjkARDzZ8xCW/a8jUg81xpnsYPxj9f3QeZvRP+6U1KE7lew/32RT7WIvihVfaRo2bPinS00VFRVhxIgRWLJkCYKCgmq8vzupNVbEtitBeqrebn16qh6t4oslisr1mLdC8jaJ8PqxGJYH/YGqbnZlEuH1QyFEPwFiE2/3x+ciXoIVfTucgs7bhEOZ4YgKLkTdgBLsOVHfto3JosL+05Fo2+iyhJE6l+I+31D2BL0aHbP/s7vd1db48ePxyCOPoG/fvpg9e/afbms0Gu3OBDAYDE6P588EBFugUgP5V+z/2/Jz1QgKM7s1Fndi3srI22tnCVBkhbW//TwcYVcJ1HOuAEYRCFbBPDccCFRJFKXzNIm4isUT18FbbUFpuQavLX0QZy8HoW2jbADAtUIfu+2vFfogIrhIilBdQmmfbwCKPvWuRsW+WbNmdyz4165dq/brrVmzBvv27cPevXurtX1ycjJmzZpV7dd3FfGWH7ggwKM/BNXFvCvINW+vDUUQ7/EB6tr/WRDb62D6KBJCgRVeGwqhnp0L04JIIMizC35mbh2Meu8x+PuU4/52Z/DmX3/GuA8ftT2vlJ+7UvJUuhoV+1mzZiEwMNApb5yVlYWXX34Zmzdvhk6nq9Y+06dPx+TJk22PDQYDoqOjnRJPdRiuqWAxA0Gh9t96A+uakZfrtNsM3HWYtwLyvmyGsL8M5pmhlZ/z8QLqeUGsB1haaeE16gK8NhbB+lfn/C2QitmiwvmrFTn8fj4ULaNz8eR9h7Dypw4AgJCAUlwtvDHKEeRfimuFvlKE6hKK+nz/gdfGr6Zhw4YhLCzMKW+cnp6OnJwcdOrUybbOYrFg27ZtWLRoEYxGI1Qq+85Bq9VCq5XulB+zyQsnD/qiY89C7Nx44w9dx56F+HWTZ//h+zPMW/55qzYVAXVUELv43HljAILJg//q3YYgiNCoLbh4TY8rBl90bnYeJy7UBQCoVRbENbmED//bReIonUdJn28bDuPfmbOP1/fp0weHDh2yW/fMM8+gRYsWmDZtWqVCf7f4ZnFdvLogCycO+uBYmh8eHnkVYfVM+H5FiNShuRTzlnHeVhFem4pg7ecHqG76PS+1QrWqANauvhBDVBAMFnitLwJyzbD29OwOd+yA3fj19wa4nO8PP205+sadRlyTS5i05GEAAr7Y1haj+uzH+dxAZF0JxKg++1FWrsbm/U2lDt2pFPH5JgA1KPbirQd2HKTX69GmTRu7dX5+fggJCam0/m6Suj4I+iALRky6jOAwMzKP6/DGyBjkXJDP7OSqMG/55i3sK4OQY4HloVsupKISIGSZoN6SCxgsgF4Fsbk3zO9HQGzk2fkH60sxc/hPCAkoQVGpN05fCsGkJQ9j7x8z8D/7uT20GjOm/GU79D5GHD0XhoTFj6DE6Nl530oJn287Cu7sBdHZVdwBvXv3RocOHTB//vxqbW8wGBAYGIjeGAy1oHFtcEQSKd9S81Na5aDs35FSh+B2Aat3SR2C25lFE7biWxQUFCAgIMAl73G9VjSflASVtnpzxKpiMZbh+PuvuzRWV7mrZmFs3bpV6hCIiEiuFNzZ1/hGOERERORZ7qrOnoiIyGUU3Nmz2BMRkSIo+Tx7DuMTERHJHIs9EREpg4S3uK3qNu6iKCIxMRFRUVHw8fFB7969ceTIkdq/yZ9gsSciIkWQ6q53t7uN+zvvvIN58+Zh0aJF2Lt3LyIiItCvXz8UFhY6IVt7LPZEREQ1YDAY7Jab78Z6q9vdxl0URcyfPx8zZszA0KFD0aZNGyxfvhwlJSVYtWqV02NmsSciImVw0jB+dHQ0AgMDbUtycvJt3/Lm27jfLCMjA9nZ2ejfv79tnVarRa9evbBz506npHszzsYnIiJlcNKpd1lZWXZX0LvdDdr+7Dbu2dnZAIDw8HC79eHh4cjMzHQgyKqx2BMREdVAQEDAHS+XW93buN96kzlRFJ1+4zmAw/hERKQQghOW6rr5Nu5qtRpqtRqpqalYsGAB1Gq1raO/3uFfl5OTU6nbdwYWeyIiUgY3nnp3/TbuBw4csC3x8fEYMWIEDhw4gMaNGyMiIgJbtmyx7VNeXo7U1FR069bNCcna4zA+EREpgjuvoFed27gnJCQgKSkJsbGxiI2NRVJSEnx9fTF8+PDaB3kbLPZEREQSmDp1KkpLSzFu3Djk5eWhS5cu2Lx5M/R6vdPfi8WeiIiUQeIb4dx6G3dBEJCYmIjExETHXrgaWOyJiEg5PPhmNo7gBD0iIiKZY2dPRESKoORb3LLYExGRMkh8zF5KHMYnIiKSOXb2RESkCBzGJyIikjsO4xMREZFcsbMnIiJF4DA+Ed21TB9HSB2CJEpGFkgdgtsFrJY6AplT8DA+iz0RESmDgos9j9kTERHJHDt7IiJSBB6zJyIikjsO4xMREZFcsbMnIiJFEEQRglj79tyRfaXGYk9ERMrAYXwiIiKSK3b2RESkCJyNT0REJHccxiciIiK5YmdPRESKwGF8IiIiuVPwMD6LPRERKYKSO3sesyciIpI5dvZERKQMHMYnIiKSP08eincEh/GJiIhkjp09EREpgyhWLI7s76FY7ImISBE4G5+IiIhki509EREpA2fjExERyZtgrVgc2d9TcRifiIhI5tjZ18LAUVfw+Iu5CA4zIfOEDh+9FYXDe/ylDsvlmLf88h7Zbz96tTuLhuH5MJpUOJQRjpT1XZCVU8e2jY+3CWMf3Y372mUi0LcMl67p8dW2Nli3vZV0gTvI66oJ+hU50O4rhmC0whzljYKXImFu6gMAEEqt8F+ZA93uQngVWmAJ06D4kWCUDgiSOHLnk/PnuxIFD+Ozs6+hXo/mYeysi1i9IAzj+jfD4d1+mP15BkLrlUsdmksxb3nmHdf0Er75pRXGzBuMSf96BCovEe+P+wE6b5NtmwlDf0WXlufxjxX3Y0TSE/hya1sk/GUHerQ9K13gDhCKLAh5LROiSkDem9G4srAxCp8Jh+insm2j//dlaPcVoSAhClcWNkbxoGAELMmGdnehhJE7n9w/37e6PhvfkcVTSVrsExMTIQiC3RIRESFlSHc09IUr2LQ6GBtXhSDrlA4fzayH3IsaDHzqqtShuRTzlmfer6Q8jA17miMjOxinLoYgeVUvRAQXoXn0Fds2bRpdxoY9zbD/VBSyr+mxfmdLnL4YghbRuRJGXnt+31yFpa4aholRMDXzgSXcG+Xt/WCJ9LZtozleitL7A1He1g+WcG+UPhgEcyMdNKdKJYzc+eT++a7k+nn2jiweSvLOvnXr1rh06ZJtOXTokNQh3ZZaY0VsuxKkp+rt1qen6tEqvliiqFyPeSsnbz9dRUdnKNHa1h08E4EebTJRN7AYgIi42IuIDi3Ant+jJYrSMbo9hTA19UGdd84jdNQJhEw6A5/NeXbbmFr6QLe3CF5XTYAowvtQMVQXy2GMk8/wthI/30om+TF7tVpd7W7eaDTCaDTaHhsMBleFVaWAYAtUaiD/iv1/W36uGkFhZrfG4k7MWyl5i5jwf7/it9MRyLgUbFs7/+tumDZsG9b943OYLQKsooC5q3vi4Jm7exTudlSXTfDdmIfiR4NR9FhdaE6WIuCTyxA1AsrurwMAMDwXgcAPLyFs9CmIKgCCgILxkTC18pU0dmdS3udb2RfVkbzYnzx5ElFRUdBqtejSpQuSkpLQuHHjKrdNTk7GrFmz3BxhZbeO5AgCPHriRnUx7wpyzXvy4zvQJOoaxn3wqN36x3sdRutGOZi2+EFkX/NH+yaX8MrjO3C1wBdpJ+pLFK0DRBGmJj4o+lsYAMDcWAf1OSN8N+bbir3v99egOV6KvNfrwxKmgfeREgR8nA1rsBrl7f0kDN75lPL5BsAJelLp0qULVqxYgU2bNmHJkiXIzs5Gt27dcPVq1ceLpk+fjoKCAtuSlZXl1ngN11SwmIGgUPtvvYF1zcjLlfx7k8swb/nnnfCXHejeJhMTFw5Ebv6NoWpvjRkvDNyLhWu7Ysfhhjh9MQTf/NIGP+5vjL/2OShhxLVnDVLDHO1tt85cXwtV7h+TEo1W6D/LQeGzYTDeo4e5kQ4ljwSjrIcefuvkcyxbSZ9vkrjYDxgwAH/5y1/Qtm1b9O3bF99//z0AYPny5VVur9VqERAQYLe4k9nkhZMHfdGxp/2M3I49C3E0TV7f9m/GvOWct4hJj21Hr/YZeHnRQFy6Zv87pVZZoVFbK3V/VqsAwUPHNMtb+EJ9wX62ufpiOSyhGgCAYBEhmAFREOx39BIAD76oyq2U8fm2p+TZ+HfV1zc/Pz+0bdsWJ0+elDqU2/pmcV28uiALJw764FiaHx4eeRVh9Uz4fkWI1KG5FPOWZ96vPL4DfTudwvRP+qOkTINgfQkAoKjMG+UmNUrKvLH/ZCTGDd4No0mN7Gv+6ND0Eh7qfBIL13WVOPraKX40GCGvnYXff66grEcANCdK4bM5D4ZxkQAA0VeF8ta+0C/PQaG3UDGMf7gEPlsLYHgmXOLonUvun+9KeNe7u4PRaMSxY8dw3333SR3KbaWuD4I+yIIRky4jOMyMzOM6vDEyBjkXvO+8swdj3vLM+//uOwoAWDTxv3br53zWCxv2NAcAzFzWB2MG7cFbT/2EAF8jsvP8sfj7zli3vaXb43UGc6wP8l+rD/+VufD/8gos4RoUjg5HWa9A2zb5U+rBf2UOAt+/CK8iCyyhGhSOCEXpQ3WkC9wF5P75phsEUZTuq8qUKVMwaNAgNGjQADk5OZg9ezZSU1Nx6NAhNGzY8I77GwwGBAYGojcGQy1o3BAxkfsVP9ZF6hAkUTjSvWfb3A0ihhyTOgS3M4smbMW3KCgocNmh2eu1ouuAv0Ot0dX6dcymMvy64S2Xxuoqknb258+fx1//+ldcuXIFoaGhuPfee7Fr165qFXoiIqIaUfBsfEmL/Zo1a6R8eyIiIkW4q47ZExERuQovqkNERCR3VrFicWR/D8ViT0REyqDgY/aS3wiHiIiIXIudPRERKYIAB4/ZOy0S92OxJyIiZVDwFfQ4jE9ERCRz7OyJiEgReOodERGR3HE2PhEREckVO3siIlIEQRQhODDJzpF9pcbOnoiIlMHqhKUGkpOT0blzZ+j1eoSFhWHIkCE4fvy43TaiKCIxMRFRUVHw8fFB7969ceTIEQeSrBqLPRERkQukpqZi/Pjx2LVrF7Zs2QKz2Yz+/fujuLjYts0777yDefPmYdGiRdi7dy8iIiLQr18/FBYWOjUWDuMTEZEiuHsYf+PGjXaPly5dirCwMKSnp6Nnz54QRRHz58/HjBkzMHToUADA8uXLER4ejlWrVmHMmDG1jvVW7OyJiEgZRCcsAAwGg91iNBqr9fYFBQUAgODgYABARkYGsrOz0b9/f9s2Wq0WvXr1ws6dOx3L9RYs9kREpAzXr6DnyAIgOjoagYGBtiU5Obkaby1i8uTJ6NGjB9q0aQMAyM7OBgCEh4fbbRseHm57zlk4jE9ERFQDWVlZCAgIsD3WarV33Oell17CwYMHsX379krPCYL9VfdFUay0zlEs9kREpAjOuoJeQECAXbG/kwkTJmD9+vXYtm0b6tevb1sfEREBoKLDj4yMtK3Pycmp1O07isP4RESkDE4axq/+24l46aWX8M033+Cnn35CTEyM3fMxMTGIiIjAli1bbOvKy8uRmpqKbt26OSXl69jZExERucD48eOxatUqfPvtt9Dr9bbj8IGBgfDx8YEgCEhISEBSUhJiY2MRGxuLpKQk+Pr6Yvjw4U6NhcWeiIgUQbBWLI7sXxMpKSkAgN69e9utX7p0KZ5++mkAwNSpU1FaWopx48YhLy8PXbp0webNm6HX62sfaBVY7ImISBncfD97sRrbC4KAxMREJCYm1jKo6uExeyIiIpljZ090l/P7arfUIUjC7yupI3C/TRcPSB2C2xkKrQhq5qY3U/AtblnsiYhIEXjXOyIiIpItdvZERKQMbp6gdzdhsSciImUQUeN70lfa30Ox2BMRkSLwmD0RERHJFjt7IiJSBhEOHrN3WiRux2JPRETKoOAJehzGJyIikjl29kREpAxWAIKD+3soFnsiIlIEzsYnIiIi2WJnT0REyqDgCXos9kREpAwKLvYcxiciIpI5dvZERKQMCu7sWeyJiEgZeOodERGRvPHUOyIiIpItdvZERKQMPGZPREQkc1YREBwo2FbPLfYcxiciIpI5dvZERKQMHMYnIiKSOweLPTy32HMYn4iISObY2dfCwFFX8PiLuQgOMyHzhA4fvRWFw3v8pQ7L5Zi3cvJWYs6AvPI+tMsP//kwDCcP+eLaZQ1mfpqBbgMKbM+LIvDZexH44fMQFBWo0CKuBOOTzqNR8zK71zma5otlcyPx+z5fqDVAk9almP3ZaWh9PLDLVfAwvuSd/YULFzBy5EiEhITA19cXHTp0QHp6utRh3VavR/MwdtZFrF4QhnH9m+Hwbj/M/jwDofXKpQ7NpZi3cvJWYs6A/PIuK/FC49alGD/nfJXPf/mvMHyzOBTj55zHwh9OICjUhOnDmqCk6EZZOJrmixkjmqBTz0Is+OEkFv5wHI8+kwtB8spRS1bR8cVDSfojy8vLQ/fu3aHRaLBhwwYcPXoU7733HurUqSNlWH9q6AtXsGl1MDauCkHWKR0+mlkPuRc1GPjUValDcynmrZy8lZgzIL+8Oz9QiKenZaPHwwWVnhNFYN0noRg28TJ6PFyARi3KMOWDczCWeuHntUG27T5OrIcho3Px5IQcNGpehnqNy3HfwAJ4az236CmVpMV+7ty5iI6OxtKlS3HPPfegUaNG6NOnD5o0aSJlWLel1lgR264E6al6u/XpqXq0ii+WKCrXY97KyVuJOQPKyzv7nDeu5WjQqVehbZ23VkTbe4twNM0PAJB/RY3f9/mhTogZCYNi8WS71pgytCkO7/aTKmzHiVbHFw8labFfv3494uPj8fjjjyMsLAxxcXFYsmTJbbc3Go0wGAx2izsFBFugUlf8EtwsP1eNoDCzW2NxJ+atnLyVmDOgvLyv5VTkGRRqslsfFGpC3h/PXcr0BgCsnBeBASOuYs7nZ9C0bQlee7IJLpzxdm/AznL9mL0ji4eStNifOXMGKSkpiI2NxaZNmzB27FhMnDgRK1asqHL75ORkBAYG2pbo6Gg3R1zh1p+3IMCTz8ioNuZdQQl5KzFnQIF533IHOFEUbOusfzSxD4+8igeHXUPTtqUYO+si6jcxYtOaEPfG6Sw8Zi8Nq9WKjh07IikpCXFxcRgzZgyef/55pKSkVLn99OnTUVBQYFuysrLcGq/hmgoWMxAUav9NP7CuGXm58j2xgXkrJ28l5gwoL+/gP0Yr8nI0duvzr6ht/wch4RX/NmxmPzs/umkZci7Y70d3P0mLfWRkJFq1amW3rmXLljh37lyV22u1WgQEBNgt7mQ2eeHkQV907Flot75jz0LbcS45Yt7KyVuJOQPKyzuiQTmCw0zYt+3GHAVTuYBDu/xtcxTCo8sRElGO86e1dvteOKNFWH374X+PoeBhfEm/snbv3h3Hjx+3W3fixAk0bNhQooju7JvFdfHqgiycOOiDY2l+eHjkVYTVM+H7FR46rFVNzFs5eSsxZ0B+eZcWe+Fixo1CnZ3ljdOHfaCvY0ZYfROGPJeLNQvDUa+xEfVijFi9IBxaHyvu/788ABWHMB57MRcr341A41alaNy6FP/7TzCyTuvwxpKzEmXlIBEOnmfvtEjcTtJiP2nSJHTr1g1JSUl44oknsGfPHixevBiLFy+WMqw/lbo+CPogC0ZMuozgMDMyj+vwxsgY5Fzw0Akr1cS8lZO3EnMG5Jf3id98MfWxprbHHyfWAwD0e+Iapsw/hyfG56C8zAuLptdH4R8X1UlefRq+/jdmnA99PhemMgEfzayHwnwVGrcqQ/Lq04hq5JnXHlAyQRSlHZf473//i+nTp+PkyZOIiYnB5MmT8fzzz1drX4PBgMDAQPTGYKgFHkMiIs+26eIBqUNwO0OhFUHNzqCgoMBlh2av14q+ES9A7VX7L29mazn+l73YpbG6iuQzTwYOHIiBAwdKHQYREcmd1QrAgXPlrTzPnoiIiO5Sknf2REREbqHgG+Gw2BMRkTIouNhzGJ+IiEjm2NkTEZEyWEU4dLK8B18ul8WeiIgUQRStEB24c50j+0qNxZ6IiJRBdPBmNjxmT0RERHcrdvZERKQMooPH7D24s2exJyIiZbBaAcGB4+4efMyew/hEREQyx86eiIiUgcP4RERE8iZarRAdGMb35FPvOIxPREQkc+zsiYhIGTiMT0REJHNWERCUWew5jE9ERCRz7OyJiEgZRBGAI+fZe25nz2JPRESKIFpFiA4M44ss9kRERHc50QrHOnueekdERERV+PDDDxETEwOdTodOnTrhl19+cXsMLPZERKQIolV0eKmpL774AgkJCZgxYwb279+P++67DwMGDMC5c+dckOHtsdgTEZEyiFbHlxqaN28eRo8ejeeeew4tW7bE/PnzER0djZSUFBckeHsefcz++mQJM0wOXSeBiOhuYCj03GPCtWUoqsjZHZPfHK0VZpgAAAaDwW69VquFVquttH15eTnS09Px2muv2a3v378/du7cWftAasGji31hYSEAYDt+kDgSIiLHBTWTOgLpFBYWIjAw0CWv7e3tjYiICGzPdrxW+Pv7Izo62m7dzJkzkZiYWGnbK1euwGKxIDw83G59eHg4srOzHY6lJjy62EdFRSErKwt6vR6CILj1vQ0GA6Kjo5GVlYWAgAC3vreUlJi3EnMGlJm3EnMGpM1bFEUUFhYiKirKZe+h0+mQkZGB8vJyh19LFMVK9aaqrv5mt25f1Wu4mkcXey8vL9SvX1/SGAICAhT1R+E6JeatxJwBZeatxJwB6fJ2VUd/M51OB51O5/L3uVndunWhUqkqdfE5OTmVun1X4wQ9IiIiF/D29kanTp2wZcsWu/VbtmxBt27d3BqLR3f2REREd7PJkyfjb3/7G+Lj49G1a1csXrwY586dw9ixY90aB4t9LWm1WsycOfOOx2rkRol5KzFnQJl5KzFnQLl5u8OTTz6Jq1ev4u9//zsuXbqENm3a4IcffkDDhg3dGocgevLFfomIiOiOeMyeiIhI5ljsiYiIZI7FnoiISOZY7ImIiGSOxb6Gtm3bhkGDBiEqKgqCIGDdunVSh+RyycnJ6Ny5M/R6PcLCwjBkyBAcP35c6rBcLiUlBe3atbNdaKRr167YsGGD1GG5VXJyMgRBQEJCgtShuFRiYiIEQbBbIiIipA7LLS5cuICRI0ciJCQEvr6+6NChA9LT06UOi5yMxb6GiouL0b59eyxatEjqUNwmNTUV48ePx65du7BlyxaYzWb0798fxcXFUofmUvXr18fbb7+NtLQ0pKWl4YEHHsDgwYNx5MgRqUNzi71792Lx4sVo166d1KG4RevWrXHp0iXbcujQIalDcrm8vDx0794dGo0GGzZswNGjR/Hee++hTp06UodGTsbz7GtowIABGDBggNRhuNXGjRvtHi9duhRhYWFIT09Hz549JYrK9QYNGmT3eM6cOUhJScGuXbvQunVriaJyj6KiIowYMQJLlizB7NmzpQ7HLdRqtWK6+evmzp2L6OhoLF261LauUaNG0gVELsPOnmqsoKAAABAcHCxxJO5jsViwZs0aFBcXo2vXrlKH43Ljx4/HI488gr59+0oditucPHkSUVFRiImJwbBhw3DmzBmpQ3K59evXIz4+Ho8//jjCwsIQFxeHJUuWSB0WuQCLPdWIKIqYPHkyevTogTZt2kgdjssdOnQI/v7+0Gq1GDt2LNauXYtWrVpJHZZLrVmzBvv27UNycrLUobhNly5dsGLFCmzatAlLlixBdnY2unXrhqtXr0odmkudOXMGKSkpiI2NxaZNmzB27FhMnDgRK1askDo0cjIO41ONvPTSSzh48CC2b98udShu0bx5cxw4cAD5+fn4+uuvMWrUKKSmpsq24GdlZeHll1/G5s2b3X6HMCndfGiubdu26Nq1K5o0aYLly5dj8uTJEkbmWlarFfHx8UhKSgIAxMXF4ciRI0hJScFTTz0lcXTkTOzsqdomTJiA9evX4+eff5b81sLu4u3tjaZNmyI+Ph7Jyclo3749PvjgA6nDcpn09HTk5OSgU6dOUKvVUKvVSE1NxYIFC6BWq2GxWKQO0S38/PzQtm1bnDx5UupQXCoyMrLSF9eWLVvi3LlzEkVErsLOnu5IFEVMmDABa9euxdatWxETEyN1SJIRRRFGo1HqMFymT58+lWahP/PMM2jRogWmTZsGlUolUWTuZTQacezYMdx3331Sh+JS3bt3r3Qa7YkTJ9x+kxZyPRb7GioqKsKpU6dsjzMyMnDgwAEEBwejQYMGEkbmOuPHj8eqVavw7bffQq/XIzs7GwAQGBgIHx8fiaNznddffx0DBgxAdHQ0CgsLsWbNGmzdurXS2QlyotfrK83F8PPzQ0hIiKznaEyZMgWDBg1CgwYNkJOTg9mzZ8NgMGDUqFFSh+ZSkyZNQrdu3ZCUlIQnnngCe/bsweLFi7F48WKpQyNnE6lGfv75ZxFApWXUqFFSh+YyVeULQFy6dKnUobnUs88+KzZs2FD09vYWQ0NDxT59+oibN2+WOiy369Wrl/jyyy9LHYZLPfnkk2JkZKSo0WjEqKgocejQoeKRI0ekDsstvvvuO7FNmzaiVqsVW7RoIS5evFjqkMgFeItbIiIimeMEPSIiIpljsSciIpI5FnsiIiKZY7EnIiKSORZ7IiIimWOxJyIikjkWeyIiIpljsSciIpI5FnsiByUmJqJDhw62x08//TSGDBni9jjOnj0LQRBw4MCB227TqFEjzJ8/v9qvuWzZMtSpU8fh2ARBwLp16xx+HSKqHRZ7kqWnn34agiBAEARoNBo0btwYU6ZMQXFxscvf+4MPPsCyZcuqtW11CjQRkaN4IxySrYceeghLly6FyWTCL7/8gueeew7FxcVISUmptK3JZIJGo3HK+wYGBjrldYiInIWdPcmWVqtFREQEoqOjMXz4cIwYMcI2lHx96P3f//43GjduDK1WC1EUUVBQgBdeeAFhYWEICAjAAw88gN9++83udd9++22Eh4dDr9dj9OjRKCsrs3v+1mF8q9WKuXPnomnTptBqtWjQoAHmzJkDALbbBcfFxUEQBPTu3du239KlS9GyZUvodDq0aNECH374od377NmzB3FxcdDpdIiPj8f+/ftr/H80b948tG3bFn5+foiOjsa4ceNQVFRUabt169ahWbNm0Ol06NevH7Kysuye/+6779CpUyfodDo0btwYs2bNgtlsrnE8ROQaLPakGD4+PjCZTLbHp06dwpdffomvv/7aNoz+yCOPIDs7Gz/88APS09PRsWNH9OnTB9euXQMAfPnll5g5cybmzJmDtLQ0REZGVirCt5o+fTrmzp2LN998E0ePHsWqVasQHh4OoKJgA8D//vc/XLp0Cd988w0AYMmSJZgxYwbmzJmDY8eOISkpCW+++SaWL18OACguLsbAgQPRvHlzpKenIzExEVOmTKnx/4mXlxcWLFiAw4cPY/ny5fjpp58wdepUu21KSkowZ84cLF++HDt27IDBYMCwYcNsz2/atAkjR47ExIkTcfToUXz88cdYtmyZ7QsNEd0FJL7rHpFLjBo1Shw8eLDt8e7du8WQkBDxiSeeEEVRFGfOnClqNBoxJyfHts2PP/4oBgQEiGVlZXav1aRJE/Hjjz8WRVEUu3btKo4dO9bu+S5duojt27ev8r0NBoOo1WrFJUuWVBlnRkaGCEDcv3+/3fro6Ghx1apVduv+8Y9/iF27dhVFURQ//vhjMTg4WCwuLrY9n5KSUuVr3axhw4bi+++/f9vnv/zySzEkJMT2eOnSpSIAcdeuXbZ1x44dEwGIu3fvFkVRFO+77z4xKSnJ7nVWrlwpRkZG2h4DENeuXXvb9yUi1+Ixe5Kt//73v/D394fZbIbJZMLgwYOxcOFC2/MNGzZEaGio7XF6ejqKiooQEhJi9zqlpaU4ffo0AODYsWMYO3as3fNdu3bFzz//XGUMx44dg9FoRJ8+faodd25uLrKysjB69Gg8//zztvVms9k2H+DYsWNo3749fH197eKoqZ9//hlJSUk4evQoDAYDzGYzysrKUFxcDD8/PwCAWq1GfHy8bZ8WLVqgTp06OHbsGO655x6kp6dj7969dp28xWJBWVkZSkpK7GIkImmw2JNs3X///UhJSYFGo0FUVFSlCXjXi9l1VqsVkZGR2Lp1a6XXqu3pZz4+PjXex2q1AqgYyu/SpYvdcyqVCgAgimKt4rlZZmYmHn74YYwdOxb/+Mc/EBwcjO3bt2P06NF2hzuAilPnbnV9ndVqxaxZszB06NBK2+h0OofjJCLHsdiTbPn5+aFp06bV3r5jx47Izs6GWq1Go0aNqtymZcuW2LVrF5566inbul27dt32NWNjY+Hj44Mff/wRzz33XKXnvb29AVR0wteFh4ejXr16OHPmDEaMGFHl67Zq1QorV65EaWmp7QvFn8VRlbS0NJjNZrz33nvw8qqYvvPll19W2s5sNiMtLQ333HMPAOD48ePIz89HixYtAFT8vx0/frxG/9dE5F4s9kR/6Nu3L7p27YohQ4Zg7ty5aN68OS5evIgffvgBQ4YMQXx8PF5++WWMGjUK8fHx6NGjBz7//HMcOXIEjRs3rvI1dTodpk2bhqlTp8Lb2xvdu3dHbm4ujhw5gtGjRyMsLAw+Pj7YuHEj6tevD51Oh8DAQCQmJmLixIkICAjAgAEDYDQakZaWhry8PEyePBnDhw/HjBkzMHr0aLzxxhs4e/Ys3n333Rrl26RJE5jNZixcuBCDBg3Cjh078NFHH1XaTqPRYMKECViwYAE0Gg1eeukl3Hvvvbbi/9Zbb2HgwIGIjo7G448/Di8vLxw8eBCHDh3C7Nmza/6DICKn42x8oj8IgoAffvgBPXv2xLPPPotmzZph2LBhOHv2rG32/JNPPom33noL06ZNQ6dOnZCZmYkXX3zxT1/3zTffxCuvvIK33noLLVu2xJNPPomcnBwAFcfDFyxYgI8//hhRUVEYPHgwAOC5557DJ598gmXLlqFt27bo1asXli1bZjtVz9/fH9999x2OHj2KuLg4zJgxA3Pnzq1Rvh06dMC8efMwd+5ctGnTBp9//jmSk5Mrbefr64tp06Zh+PDh6Nq1K3x8fLBmzRrb8w8++CD++9//YsuWLejcuTPuvfdezJs3Dw0bNqxRPETkOoLojIN/REREdNdiZ09ERCRzLPZEREQyx2JPREQkcyz2REREMsdiT0REJHMs9kRERDLHYk9ERCRzLPZEREQyx2JPREQkcyz2REREMsdiT0REJHP/DwKQSBA9ABf2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acuracia_teste = melhor_arvore.score(X_test_cut, y_test)\n",
    "\n",
    "print(f'Acurácia: {acuracia_teste*100:.2f}')\n",
    "ConfusionMatrixDisplay.from_estimator(melhor_arvore, X_test_cut, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "WoXsCWxPdLtf",
    "outputId": "dce0a89b-884f-49bd-8f33-23cfd4c2b12f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia: 81.49\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Avaliei a árvore encontrada no item anterior na base de treino também\n",
    "\n",
    "acuracia_teste = melhor_arvore.score(X_train_cut, y_train)\n",
    "\n",
    "print(f'Acurácia: {acuracia_teste*100:.2f}')\n",
    "ConfusionMatrixDisplay.from_estimator(melhor_arvore, X_train_cut, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}